The recent sluggishness in consumer spending has drawn widespread attention, with some top-tier cities even reporting negative year-on-year growth in total retail sales of consumer goods (social retail). During the first half of this year, the year-on-year growth rates for total retail sales in Beijing, Shanghai, Guangzhou, and Shenzhen were -2.2%, 0.7%, 2.9%, and 1.2%, respectively. This paints a picture of generally low growth rates paired with pronounced divergence among these cities. Notably, Guangzhou's retail growth outpaced the national average, while Beijing slipped into negative territory, creating a gap of 5.1 percentage points between the two.
What fundamental reasons lie behind this pressure on consumption in top-tier cities and their uneven performance? What is the real story of consumer spending in these metropolises? And what lessons can be drawn for policies aimed at boosting consumption? This article aims to address these critical questions.
Retail Disparities Among Top-Tier Cities: Significant Differences in Overall Growth and Category Performance
Looking at the broader picture for the first half of 2026, retail sales growth in the four major cities was generally subdued but clearly divergent. Guangzhou saw a 2.9% increase, Shenzhen 1.2%, and Shanghai 0.7%, while Beijing experienced a 2.2% decline. The difference in growth between Guangzhou and Beijing was a substantial 5.1 percentage points. In terms of trends, Beijing's retail performance has been persistently weak, Shanghai's growth is decelerating, and Guangzhou and Shenzhen are showing relative strength. Beijing's retail sales have been in negative territory since 2025, with the decline briefly narrowing in early 2026 before widening again. Shanghai saw a rebound in early 2026 due to a low base, but growth fell to 0.7% in the first half as the base effect waned. In contrast, Guangzhou and Shenzhen maintained positive growth despite a higher comparison base from the previous year, with recent momentum picking up.
The divergence becomes even more stark when examining specific product categories. In the automobile sector, Beijing and Shanghai face significant constraints due to license plate acquisition, leading to notably weak auto sales. Guangzhou, however, has boosted supply by increasing license plate quotas, resulting in sustained growth. Nationally, retail sales of automobiles by enterprises above a designated size fell 12.6% year-on-year in the first half. Beijing dropped 17.2%, Shanghai 11.7%, but Guangzhou grew 8.2%. For communication equipment, Shenzhen is far ahead while Beijing and Shanghai lag. National growth in this category was 14.4%, with Shenzhen surging 42.4% and Guangzhou 8.1%, against declines of 0.4% in Beijing and 6.8% in Shanghai. In apparel and cosmetics, Guangzhou is growing rapidly, and Shenzhen's cosmetics sales are also strong. National growth for clothing, shoes, hats, and textiles was 6.7%, with Guangzhou up 30.8%, Shanghai 7.2%, and Beijing 1.0% (Shenzhen data for Jan-May shows 6.6% growth). Cosmetics grew 6.3% nationally, with Guangzhou up 10.6%, Shenzhen 11.2%, Shanghai 2.7%, and Beijing down 6.1%. Interestingly, despite Beijing's overall retail pressure, its gold, silver, and jewelry retail sales jumped 21.4% in the first half, significantly outpacing the national rate of 1.9% and Shanghai's 3.9%.
Clearly, there is no single "strong or weak" ranking that applies to all products across these four cities. There is significant divergence between different categories within a single city and between cities for the same category. Relying solely on overall resident consumption capacity and willingness fails to fully explain these inter-city differences. Therefore, to understand why consumer spending in top-tier cities is uneven, we must move beyond comparing overall retail growth and delve into the specific mechanisms affecting different product categories.
Three Mechanisms Driving Retail Divergence: Statistical Attribution, Policy Differences, and Supply-Demand Fit
Comprehensively, the current divergence in retail sales among the four cities can be attributed to three key mechanisms. First, statistical attribution, where cross-regional adjustments by business entities and differing business models alter the statistical distribution of retail sales across regions, primarily affecting communication equipment and gold, silver, and jewelry. Second, policy differences, where the scope of trade-in subsidies and the ease of implementation directly influence growth differences in home appliances. More convenient subsidy methods, like "instant discounts at purchase," are better at releasing demand, while complex methods like lotteries or coupon drawing may hinder timely demand release. Beijing's strict license plate quotas and Shanghai's high acquisition costs suppress auto consumption, whereas Guangzhou's increased quotas and subsidies help release related demand. Third, supply-demand fit, where the alignment of goods and service supply with consumer demand impacts growth, mainly seen in apparel, cosmetics, and dining.
Statistical Attribution: How Business Adjustments and Models Shape Regional Retail Data for Communication Equipment and Jewelry
Total retail sales of consumer goods are tallied from the sales side, reflecting the retail value of goods and catering revenue achieved by business units, rather than being attributed based on where the final consumer is located. For industries or products where cross-regional sales are common, such as e-commerce and direct online sales, a business entity in one region can sell to consumers nationwide. Thus, the location of the business unit and the final consumer are not always the same. If a company's business model changes, or if its operations and sales activities are reallocated between regions, it can alter the statistical distribution of related retail sales, even if end-user demand remains constant. For the national total, as long as sales occur domestically, regional reshuffling typically doesn't change the overall figure. However, for a specific city, it can significantly change its locally recorded retail sales. This effect is especially pronounced for products with high brand concentration and a large share of direct or online sales. Therefore, when analyzing the retail divergence among these cities, it's crucial to distinguish between changes in end-user demand and shifts in statistical attribution of sales activities. This is clearly evident in categories like communication equipment and gold, silver, and jewelry.
In communication equipment, cross-regional business adjustments may have amplified the growth gap between Beijing and Shenzhen. In the first half of 2026, national retail sales of communication equipment grew 14.4% year-on-year, maintaining a fast pace, but performance varied sharply among the top-tier cities: Shenzhen grew an exceptional 42.4%, Guangzhou 8.1%, and Beijing saw a 0.4% decline. The gap between Beijing and Shenzhen widened rapidly in the second quarter. Beijing's growth was 10.6% in Q1 and 9.5% for Jan-Apr, before a sharp fall to -0.4% for the half-year. Shenzhen rose from 7.2% in Jan-Apr to 33.4% in Jan-May, reaching 42.4% for the first half. Business and sales layout adjustments may have persistently impacted Beijing's communication equipment sales. In the first half of 2025, Beijing's sales in this category fell 24.0%, with the Beijing Municipal Bureau of Statistics noting this was "mainly due to changes in enterprise business models, with a significant increase in cross-regional business entities, impacting retail sales achieved by some units in Beijing." In 2026, the bureau again mentioned the influence of "business model transformations by some enterprises." A key reason for Beijing's continued weakness could be that some companies have shifted their operational entities to other regions, causing sales previously counted in Beijing to be recorded elsewhere. Shenzhen may illustrate the opposite side of this statistical attribution. Its e-commerce policies support manufacturing enterprises in establishing independent accounting sales companies or e-commerce settlement entities locally and provide support for newly included or rapidly growing online retailers. Data shows Shenzhen's communication equipment growth is highly concentrated regionally, with Longgang District growing 59.0% in Q1 and 57.7% in the first half. This growth was significantly above the city average even before Guangdong added high-end digital product subsidies in May, indicating strong support from key enterprises and sales entities. Shenzhen's robust consumer electronics industry base, professional sales channels, and the new provincial subsidies also helped expand sales. While available public information doesn't prove a direct transfer of sales from Beijing to Shenzhen, the situations in both cities suggest cross-regional business adjustments are likely a factor amplifying their growth gap.
For gold, silver, and jewelry, business model differences influence the regional attribution of sales. The city-level differences further indicate that whether sales enterprises use direct operations or franchising affects the statistical distribution of national sales. In a direct operation model, terminal sales nationwide may be more concentrated in the sales enterprise's home region, boosting its local retail data. Franchising disperses retail sales to the location of each franchise store, which typically better reflects the regional distribution of end sales. In the first half of 2026, Beijing's retail sales of gold, silver, and jewelry grew 21.4% year-on-year, far exceeding the national 1.9% and Shanghai's 3.9% (data for Guangzhou and Shenzhen is undisclosed). This is not a short-term phenomenon: in 2023, Beijing grew 35.0%, well above the national 13.3% and Shanghai's 14.4%; in 2024, Beijing grew 15.9% while nationally it fell 3.1%; and in 2025, Beijing grew 39.5% versus 12.8% nationally and 5.5% in Shanghai. Beijing's consistent outperformance for years is difficult to explain solely by stronger local gold demand. Differences in corporate business models and resulting sales statistical attribution likely play a significant role. Beijing is home to Caibai, a major national-level direct-sales and e-commerce retailer. In 2025, Caibai's online sales reached RMB 7.28 billion, accounting for 25.3% of its total sales. Its e-commerce subsidiary, registered and primarily operating in Beijing, achieved revenue of RMB 6.67 billion. This means a portion of Caibai's terminal sales to consumers outside Beijing via direct online channels is likely recorded by the Beijing entity, thus counting towards Beijing's retail data. The business model of a leading Shanghai jeweler, Lao Feng Xiang, differs. At the end of 2025, it had 5,355 sales outlets, of which 5,142 were franchise stores and only 213 were directly operated. Under the franchise model, terminal sales are more dispersed, with retail sales recorded by franchise entities in various locations rather than being heavily concentrated in Shanghai.
Policy Differences: How Subsidy Programs and License Plate Restrictions Influence Appliance and Auto Demand Release
Home appliances and automobiles are characterized by high transaction values, lower purchase frequency, and the ability to postpone purchases, making them highly sensitive to policy changes like subsidies and license plates. In the first half of 2026, the divergence in these two categories across top-tier cities reflects the impact of local subsidy scope, implementation pace, usage methods, and institutional constraints like license plates on demand release.
In home appliances, the scope and implementation method of subsidies influenced city performance. Nationally, retail sales of household appliances and audio-video equipment fell 7.4% year-on-year in the first half, with Shanghai down 14.2%, Beijing down 3.7%, and Guangzhou up 1.7%. The national decline is linked to reduced quotas for trade-in policies compared to the previous year and the early release of pent-up demand. Differences between cities depend more on whether local governments expanded subsidy coverage promptly, which products were included, and how convenient it was to obtain and use the subsidies. First, whether local subsidies are expanded in a timely manner directly impacts demand release when national subsidies shrink. In the first half, Beijing primarily implemented the national unified subsidy for six categories of appliances, only launching a broader smart home subsidy in July with ten new product categories like smart locks, robot vacuums, smart toilets, and smart beds. Beijing's high-efficiency appliance sales still grew over 60% in the first half, indicating growth was heavily concentrated in subsidized high-efficiency products, consistent with the impact of subsidy scope. In contrast, Guangzhou and Shenzhen expanded their effective subsidy coverage in May. Guangdong added six locally subsidized products in May, including smart locks, smart displays, smart toilets, and digital cameras. Guangzhou implemented this according to plan with a 15% subsidy per item, up to RMB 1,500. Shenzhen launched smart home product subsidies in May, covering smart beds (including mattresses), smart displays, robot vacuums, and smart toilets, also at 15% up to RMB 1,500. After Shenzhen included smart beds and mattresses, support extended into furniture and home goods, with furniture retail sales growing 19.1% in the first half. Second, how subsidy eligibility is obtained and used also affects whether consumer demand translates into actual transactions. Beijing's subsidy eligibility opens daily at 10 AM and is on a first-come, first-served basis; online eligibility is valid for the day, while offline is generally valid for the week. Shanghai's early policies involved periodic registration and lotteries for online channels and immediate lotteries offline, with online channels also switching to immediate lotteries in early June. These arrangements help control spending pace but also increase uncertainty for consumers trying to secure subsidies when they plan to purchase. For durable goods like appliances, where purchase timing is somewhat flexible, some demand may be delayed. In terms of usage, Guangzhou and Shenzhen have policies allowing direct verification or deduction at the point of sale. Some offline "national subsidy" appliance and digital purchases in Guangzhou can be verified during the purchase, with some items also stackable with brand promotions and merchant discounts. Shenzhen's smart home subsidy introduced in May operates on an "instant buy, instant enjoy" basis, allowing direct deduction at the time of purchase online or offline. Guangzhou's appliance retail sales grew 36.6% in Q1, moderating to 1.7% for the first half due to high base effects and earlier demand release, yet still higher than the national average, Beijing, and Shanghai.
In automobiles, license plate constraints and purchase subsidies jointly influence new demand. City-level differences are even more pronounced. In the first half of 2026, Beijing and Shanghai saw auto retail sales fall 17.2% and 11.7% year-on-year respectively, while Guangzhou grew 8.2%. Structurally, Beijing's new energy vehicle (NEV) sales still grew 5.2%, but traditional fuel vehicles were a major drag. Guangzhou's NEV sales grew 29.2%, driving overall auto growth. These differences are closely tied to local vehicle purchase eligibility constraints and consumption promotion policies. First, license plate and quota policies directly impact the release of new purchase demand. Beijing continues to manage new car purchases with a quota system for passenger cars, keeping 20,000 regular quotas in 2026. Regular NEV quotas remain at 80,000, with additional NEV quotas increasing from 60,000 in 2025 to 80,000. However, the supply-demand gap remains significant: as of March 8, 2026, there were 326,800 valid family applications and 566,900 individual applications for NEVs in Beijing, but the May allocation was only 119,200 for families and 34,800 for individuals, representing 2.7 and 16.3 times oversubscription, respectively. Shanghai still uses auctions for individual fuel vehicle plates, with an average winning bid around RMB 94,000 in the first half, a notable cost. Guangzhou has further increased quota supply. In 2026, on top of the 80,000 regular "ordinary car" incremental quotas, it added 120,000 more quotas, all allocated via lottery. Quotas for energy-saving and NEVs have no annual limits and can be applied for directly. Overall, Guangzhou's increased supply and direct application for NEV quotas provide more room for new demand release. Second, the target and method of purchase subsidies also vary. Beijing's 2026 replacement program primarily supports consumers selling old cars to buy NEVs, with an 8% subsidy up to RMB 15,000 based on the new car price. General fuel vehicles are not included, and support for first-time buyers without an old car is limited. Shanghai's NEV replacement subsidy is 8% up to RMB 15,000, and for fuel vehicles up to 2.0L it's 6% up to RMB 13,000, covering a broader base but mainly for replacement. In the first half, some periods required registration and public lottery for subsidy eligibility. Starting in May, Guangzhou implemented a "Guangdong Premium Purchase" direct purchase subsidy for new cars, offering RMB 2,000, RMB 4,000, and RMB 5,000 for cars priced RMB 60,000-80,000, RMB 80,000-150,000, and above RMB 150,000, respectively, without requiring disposal of an old car. This is stackable with financial institution offers, manufacturer discounts, and some district-level promotions. This allows Guangzhou's policies to cover scrap-and-replace, trade-in, first-time purchase, and additional family purchases simultaneously, combining with the 120,000 new quotas to support new demand from both eligibility and cost perspectives.
Supply-Demand Fit: How Matching Supply with Demand Shapes Apparel, Cosmetics, and Dining Growth
Unlike appliances and autos, apparel, cosmetics, and dining are subject to fewer administrative purchase restrictions. They have higher consumption frequency and faster changes in products and business formats. Therefore, differences between cities depend more on two factors: first, the core consumer base and its changing needs; second, whether the existing industrial, brand, commercial, and service supply can adapt to these changes in a timely manner.
In apparel and cosmetics, Guangzhou has a distinct supply advantage, while Shenzhen benefits from a young customer base and new supply. Nationally, retail sales of clothing, shoes, hats, textiles, and cosmetics grew 6.7% and 6.3% year-on-year, respectively, in the first half. Beijing's apparel grew only 1.0% and cosmetics fell 6.1%, clearly underperforming the national average. Guangzhou surged 30.8% and 10.6%, significantly outpacing the country. Shanghai's apparel grew 7.2%, and Shenzhen's Jan-May growth was 6.6%, close to the national average, but cosmetics showed greater divergence with Shanghai up 2.7% and Shenzhen up 11.2%. Beijing and Guangzhou present a stark contrast. Beijing's weak apparel sales align with slower growth in resident clothing expenditure. Per capita clothing spending in Beijing grew 2.9% year-on-year in the first half, below the national 4.4%. Apparel retail sales fell 4.2% in the first half of 2025 and only grew 1.0% in the first half of 2026 from a low base. Cosmetics were also weak, growing 7.6% in H1 2025 but falling 6.1% in H1 2026, resulting in near-zero cumulative growth over two years, indicating generally weak momentum. Guangzhou's growth in both categories is significantly faster than the national average, and both were positive in the same period of 2025, making a low-base explanation unlikely. Apparel and cosmetics are areas where Guangzhou's industrial, commercial, and consumer base advantages overlap heavily. Guangzhou hosts numerous apparel and cosmetics manufacturers, brand operators, professional markets, and commercial resources, forming a complete ecosystem from R&D, design, production, and brand incubation to terminal retail. This industrial and commercial agglomeration allows for faster product iteration, a richer selection of brands and products across price points, better serving different consumer levels and creating new demand through new products and niche supply. Additionally, the vast consumer hinterland of the Pearl River Delta, along with tourism and convention-related visitor flows, expands the consumer base reachable by Guangzhou's commercial supply. The comparison between Shenzhen and Shanghai further illustrates the importance of matching consumer demographics with supply. Both had similar apparel growth rates, but Shenzhen's cosmetics grew 11.2% versus Shanghai's 2.7%. A key advantage for Shenzhen is its younger population structure, potentially providing a larger base for trendy, self-pleasing consumption. Data from the seventh national census shows that in 2020, Shenzhen's population aged 15-59 was 79.5%, significantly higher than Guangzhou's 74.7%, Beijing's 68.5%, and Shanghai's 66.8%. Shenzhen's population aged 60 and over was only 5.4%, much lower than Guangzhou's 11.4%, Beijing's 19.6%, and Shanghai's 23.4%. Meanwhile, Shenzhen has been actively strengthening its first-store, first-release, and new product supply, with cosmetics being a key category. The match between young consumer demand and new commercial supply gives Shenzhen's cosmetics consumption high growth elasticity. Shanghai's situation differs as its cosmetics consumption and commercial supply are already mature. International brands, high-end cosmetics, and first-release products have long been concentrated there, and consumer brand choices and buying habits are relatively stable. Therefore, the marginal impact of new brands, products, and channel expansion is limited. Shanghai's cosmetics retail sales grew 2.9% in H1 2025 and 2.7% in H1 2026, maintaining low single-digit growth for two consecutive years, indicating its cosmetics demand is in a relatively stable growth phase. Overall, the city-level divergence in apparel and cosmetics reveals a clear supply-demand fit logic: Guangzhou's advantage comes from the strong alignment between its industrial and commercial supply and fashion consumption, with strong regional consumer attraction; Shenzhen relies on its young consumer base and expanding cosmetics supply for high demand elasticity; Beijing's underperformance is linked to relatively insufficient terminal demand; Shanghai's mature market means more stable, incremental growth.
In dining, changing demand structures have different impacts on cities with different supply structures. Dining revenue also shows clear divergence among top-tier cities, with relatively lower growth in Beijing and Shanghai and better performance in Guangzhou and Shenzhen. In 2025, dining revenue in Beijing and Shanghai fell 3.0% and 1.9% year-on-year, respectively, while Guangzhou and Shenzhen grew 3.5% and 2.5%. In the first half of 2026, the gap widened: Shanghai's dining revenue for enterprises above a designated size fell 0.2%, Beijing's grew only 0.4%, and Guangzhou and Shenzhen grew 5.8% and 5.1%. First, there are structural differences in dining formats. Beijing and Shanghai have a supply more tilted towards mid-to-high-end, full-service restaurants (zhengcan), while Guangzhou and Shenzhen lean more towards popular, high-frequency dining. Data from the fifth national economic census shows that in 2023, the share of full-service restaurant revenue in total dining revenue was 56.2% in Guangzhou, lower than Beijing's 60.1%, Shenzhen's 62.5%, and Shanghai's 63.1%. Conversely, the share of non-full-service formats (fast food, beverages, delivery, snacks) was 43.8% in Guangzhou, higher than Beijing's 39.9%, Shenzhen's 37.5%, and Shanghai's 36.9%. Guangzhou's dining market has a higher proportion of high-frequency daily consumption formats like work meals, light meals, beverages, and delivery. Although Shenzhen has a similarly high full-service share as Beijing and Shanghai, its full-service operations may be more mass-market oriented. In 2023, the indicator "full-service revenue divided by full-service employees" was RMB 240,000 and RMB 234,000 per person in Guangzhou and Shenzhen, lower than Beijing's RMB 276,000 and Shanghai's RMB 329,000. While this metric is influenced by store size, table turnover rate, and operational efficiency, and cannot be directly equated with average order value or grade, it reflects structural differences in full-service operations. Combined with market characteristics, it's reasonable to infer that Beijing and Shanghai have a higher proportion of large restaurants, hotel dining, business dining, and formal banquet formats. Shenzhen's full-service segment is likely more focused on popular local cuisines, community restaurants, mall chains, hotpot, and barbecue oriented towards residents' daily dining. Second, dining demand is undergoing two changes. One is the reduction in business receptions and banquets by enterprises and social groups. The "2025 China Catering Industry Ecological White Paper" notes a decrease in business activities in 2024 and a clear reduction in high-ticket business banquet demand. KPMG's "2026 China Catering Enterprise Development Report" also points to significant impact on high-end dining from shrinking business consumption in 2025. The other is that residents are becoming more rational and price-sensitive. The 2025 white paper shows consumers are more cautious, rational, and pragmatic in dining, valuing actual product value and cost-performance. The 2026 KPMG report shows the average order value in China's dining market fell from RMB 36.5 in 2024 to RMB 34.7 in Q3 2025, a 4.9% decline. By format, Western and Chinese full-service restaurants saw order values fall 5.1% and 4.6%, respectively, while Chinese and Western fast food only fell 1.3% and 2.4%, showing greater resilience in mass-market dining. Because of different dining supply structures, the impact of demand changes has widened the performance gap between cities. Beijing and Shanghai, with higher full-service shares, are more affected by weaker full-service demand and lower order values. Beijing's sub-category data reflects this: in H1 2026, beverage and cold drink service revenue grew 6.0%, and fast food services grew 1.4%, both higher than the city's overall dining growth of 0.4%, while full-service revenue fell 0.6% in Jan-Apr. This means Beijing's mass-market dining is still growing, with pressure concentrated in the full-service segment. In contrast, Guangzhou and Shenzhen have dining supply structures better suited to current demand changes. Guangzhou's higher share of popular dining means fast food, beverages, light meals, and delivery can directly cater to high-frequency scenarios like work meals, family dining, and friend gatherings. Shenzhen, despite a higher full-service share, may have full-service operations more oriented towards ordinary residents' daily gatherings, with less reliance on large-scale business banquets and high-ticket formal dining. Thus, in an environment of declining business demand and more rational consumer spending, Guangzhou and Shenzhen's dining revenues show stronger resilience.
Policy Recommendations
Enhance Regional Consumption Monitoring for Better Assessment Accuracy
Improve the regional consumption monitoring framework to reduce the interference of statistical attribution changes on regional consumption assessments and strengthen the ability to identify terminal consumer demand. First, strengthen monitoring of operational changes in key enterprises and industries. For sectors with significant cross-regional operations, high online sales shares, and heavy influence from leading companies, track changes in operating entities, sales entities, settlement entities, and business models. Establish a linked monitoring mechanism between key enterprise operational changes and regional consumption data to promptly identify impacts from enterprise migration, business restructuring, direct online sales, and franchise model adjustments, avoiding mistaking statistical attribution changes for demand changes. Second, expand supplementary monitoring from the demand-side perspective. Leverage data on e-commerce order delivery locations, payment settlements, and logistics to better identify the final flow of goods and services to consumers, complementing the supply-side statistics. Gradually build a regional consumption monitoring system that combines "operating entity location statistics" with "consumer demand location monitoring." Third, improve the regional consumption comprehensive evaluation system. Reduce reliance on the single indicator of total retail sales growth. Cross-validate with indicators like resident consumption expenditure, service consumption, key industry transactions, and foot traffic. Distinguish between statistical attribution changes, policy-driven effects, and genuine demand changes to improve the accuracy of assessing regional consumption situations and policy effectiveness.
Increase Precision and Coordination of Consumption Promotion Policies for Better Implementation Efficiency
Optimize policy tools based on different regions' consumption structures, market maturity, and actual constraints to better align policy scope, combinations, and implementation methods with real demand. First, enhance flexibility in local policy adjustments. Within a stable national policy framework, leave room for local governments to adjust support scope and implementation based on local consumption structures, product iteration, and demand changes. Establish mechanisms for dynamic policy evaluation and timely adjustment. For rapidly evolving sectors, adjust support catalogs and implementation arrangements based on market sales and product iteration to reduce mismatch between policy supply and market demand. Second, strengthen coordination between consumption promotion policies and related management policies. For consumption areas constrained by entry conditions and quota allocations, single price subsidies may have limited effect. Integrate demand support with relevant management policies. In the auto sector, coordinate scrap-and-replace, trade-in, direct purchase, and license plate quota policies based on each city's traffic capacity, vehicle ownership, and new demand to ensure purchase eligibility management and purchase subsidies complement each other. Third, improve convenience in the policy usage process. Optimize application, verification, redemption, and fund utilization procedures. Reduce uncertainty in the policy usage process while managing risk, employing more convenient and direct implementation methods. Also, enhance policy continuity and predictability, reasonably arrange fund release pace, and better coordinate policy implementation rhythm with consumer demand to reduce delayed or overly advanced demand.
Enhance Supply-Side Adaptation Capabilities to Cultivate Structural Consumption Growth
Place greater emphasis on the supply side's response to demand changes. Improve supply-demand matching efficiency through industrial, brand, commercial, and service supply adjustments to foster more sustainable consumption growth. First, promote the extension of industrial and commercial advantages towards brands and terminal markets. Support cities with strong manufacturing, R&D, and commercial foundations in connecting production, branding, channels, and terminal retail. Strengthen capabilities in R&D design, own-brand development, new product promotion, and direct online sales. Encourage industrial chains to extend more towards brand operations and terminal markets, transforming industrial innovation and commercial resources into consumption growth more effectively. Second, optimize product and commercial supply based on consumption demographics and demand changes. Strengthen tracking of population structure, income levels, consumption preferences, and lifestyle changes. Guide enterprises and commercial venues to adjust product mixes, brand structures, and service content promptly. For mature demand areas, rely more on niche supply, experiential enhancements, and format renewal to tap growth. For rapidly changing demand areas, improve the speed of response with new products, brands, and formats, reducing homogenized expansion. Third, promote continuous innovation in service supply and consumption scenarios. Align with the trend towards more daily, convenient, and experiential consumption. Drive traditional service sectors to adjust products, pricing, and business models, accelerate digital operations and online-offline integration, and develop more flexible and diverse service offerings. Strengthen linkages between commerce and culture, tourism, conventions, sports, and performing arts. Expand consumption scenarios through cross-industry integration, improve the conversion of foot traffic to actual spending, and enhance the linkage and sustainable operation of different consumption scenarios.
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