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Abstract

Consumption-led economic growth is crucial for enhancing economic resilience, improving social welfare, and fostering endogenous drivers for innovative development. The purpose of this study is to develop a novel multi-criteria evaluation framework to assess the level of consumption-led economic development in the Yangtze River Delta urban agglomeration. Initially, an evaluation system for consumption-led economic development is constructed across five dimensions: economic autonomy, demand structure, consumption level, consumption structure, and consumption environment. The evaluation framework based on the Vertical and Horizontal Scatter Degree and Entropy Method (VHSD-EM), is then applied to analyze the development level of consumption-led patterns in the core cities of the Yangtze River Delta in China from 2015 to 2021. The empirical results reveal significant disparities in consumption-led economic development among the cities. In 2021, Shanghai achieved the highest comprehensive score (7.83), followed by Hangzhou, Suzhou, Hefei, Ningbo, and Nanjing. The average score for the region was 7.37, suggesting that the Yangtze River Delta urban agglomeration is transitioning toward a consumption-led growth model, with some cities exhibiting characteristics of a high-mass consumption stage. However, the development stages vary across cities, reflecting differences in economic structure and policy focus. Finally, several recommendations are suggested based on the numerical analysis.

Keywords

consumption-led economy indicator system VHSD-EM Yangtze River Delta urban agglomeration multi-criteria evaluation

Introduction

Since the outbreak of the COVID-19 pandemic, there has been a significant shift in China’s economic development model, with consumption gradually becoming the primary driver of economic growth among the three major drivers. The report of the 20th National Congress of the Communist Party of China clearly stated the need to adhere to promoting high-quality development, organically integrating the implementation of the strategy to expand domestic demand with deepening supply-side structural reforms, and enhancing the endogenous power and reliability of the domestic cycle. According to Rostow’s theory of economic development, China is gradually transitioning from the stage of economic takeoff to the early stage of mature advancement, with the economic growth pattern evolving from investment-led to consumption-led (Liu & Zhu, 2021). Combining the fact that the primary contradiction in Chinese society has transformed into the contradiction between the increasing demand for a better life and the imbalance and inadequacy of development, studying the trend of consumption-led economic development in economically developed regions of China is of great significance, especially as China is currently at a critical period of consumption release.

Regarding the concept of a consumption-driven economy, scholars have proposed the notion of “new consumption.” Although there is no unified definition, a consensus has been reached on its rich connotations. Specifically, “new consumption” refers to a novel consumption model driven by the integration of new technologies—such as digital culture, “5G+,” and new energy—with online shopping and offline services, as well as “new consumption behaviors” propelled by digital consumption relationships emerging from social platforms, online media, and new consumption mediums.

Currently, the research on consumption-led economy mainly focuses on the following two aspects:

(1) Evaluation methods of consumption-led economy. Zhang et al. (2021) constructed the indicator system of consumption-led economies including eight indicators, including residents’ food consumption, residents’ housing consumption, and residents’ medical and health care consumption, and empirically studied the impact of residents’ consumption on China’s economy. Laukka et al. (2019) conducted a review concerning the consumer-led health-related online sources and their impact on consumers, which proposed a useful reference for us to study the consumption-led economies. Huang et al. (2022) measured the level of consumption-led economic growth in 31 provinces and cities in China from 2010 to 2017 and empirically tested the impact of the digital economy on consumption-led economies. Shi et al. (2021), conducted an analysis from 59 economies across different income groups from 1995 to 2018, providing a statistical description of consumption rates, the time trends of consumption upgrading, and economic development. On this basis, they conducted an empirical test on the impact of consumption on economic development. Sun (2015) based on an analysis of the characteristics of consumption-led major economies, constructed an evaluation system for scoring consumption-led major economies. This system includes four primary indicators: the structure of demand, the environment for consumption, the level of consumption, and the structure of consumption. Arellano et al. (2017) developed a new quantile-based panel data framework to study the transmission of income shocks to consumption. Meanwhile, the determination of indicator weights is an important issue in evaluation process. Sun et al. (2017) used a simple equal-weight method and linear weighted calculations to obtain the final evaluation scores for consumption-led major economies assessment. Huang et al. (2022) improved the evaluation method by adopting subjective and objective weights, considering that certain variables may play a key role in explaining the overall situation. However, they only conduct static analysis within a certain scope and cannot achieve dynamic trend comparisons of the evaluation objects.

(2) Research on the development path of consumption-led economy. Currently, the constraints on China’s economic growth have gradually shifted from supply constraints to demand constraints. For example, by separately calculating the traction coefficients of consumption and investment on economic growth, Wen and Yao (2002) verified that residents’ consumption now plays a decisive role in China’s economic growth. Yang et al. (2024) explores the contribution of China’s prosperous middle class to consumption-led economic growth based on micro level household survey data. Sommer (2007) analyzed that consumption growth appears to be highly persistent after controlling for measurement errors and transitory consumption fluctuations, which means when consumption has a positive boost to the economy, it appears to be a highly sustained process. Hong (2013) believes that there are two prerequisites for a country to transition from an Investment-driven economic development model to a consumption-driven economic development model: expanding domestic consumption demand and developing a consumption-led economy. The main point is that although consumer behavior is crucial for developing a consumption-driven economy, it is even more important for manufacturing and service enterprises to engage in independent research and development innovation. Waldfogel (2003) proposed that aggregation strengthens consumption, with market expansion and increased interpersonal interactions driving enthusiasm for consumption, thereby stimulating economic growth. This strongly supports the practical value of the integrated development of urban agglomerations.

China has a vast territory, and its economic development shows significant differences among provinces and cities at different stages of development. However, existing research on small-scale regional studies in China is relatively limited, lacking analysis of the spatiotemporal disparities in the development of consumption-led economies in domestic cities, and there is insufficient understanding of the connotation and characteristics of consumption-led economies. Against this background, focusing on typical cities in the Yangtze River Delta urban agglomeration, this study aims to deeply study the dynamic trend of economic growth mode transformation from the East’s relatively developed cities to consumption-led in nature.

Based on the comprehensive evaluation of the VHSD-EM model, this article measures the economic growth models of some cities in the Yangtze River Delta urban agglomeration, and further studies the characteristics of urban consumption level, consumption structure, and demand structure, providing objectivity and reproducibility basis for the future economic development of other underdeveloped areas.

Evaluation System for Consumption-Led Economic Development

Connotation of Consumption-Led Economy

Based on extensive reviewing of existing literature, this study redefines the connotation of a “consumption-led economy.” A consumption-led economy refers to a scenario where, in the development of the national economy, consumption has become the primary driving force of economic growth compared to investment and exports, playing a fundamental role in economic development. The constraints on economic growth have shifted from supply constraints to demand constraints, with the focus of economic development shifting to consumption, the economic development strategy transitioning to a new pattern of dual circulation, and the economic development model shifting to high-quality development. Chenery and Syrquin (1975) demonstrated that a country’s economic growth pattern is inextricably linked to its stage of economic development. As the average income level of domestic residents rises, the overall consumption rate follows a “U-shaped” pattern, initially declining before subsequently increasing. Evidence shows consumption-led growth typically emerges in higher-income economies, where residents’ income levels significantly increase, consumption structures upgrade, and social welfare guarantees improve.

Therefore, the main features of a consumption-led economic model include: highly developed economy with a high degree of autonomy; lightweight economic structure tending to rely on the service industry and high value-added industries; improved residents’ consumption capacity, significant increase in per capita disposable income (or median disposable income of residents), and higher proportion of consumption in the national economy; dominance of consumer demand and preferences in the market, clear consumer sovereignty; improvement in residents’ living standards driving the acceleration of consumption structure upgrading, and sufficient supply of goods and services promoting the formation of a buyer’s market; emphasis on consumer demand-centeredness in a consumption-led economy, driving economic development by satisfying and creating new demands, with a trend towards diversification of consumption patterns. In summary, a consumption-led economy emphasizes consumer demand centrality, and high consumption rates and diversified consumption patterns directly reflect consumption-led economic structural upgrading, service orientation, and innovation drive as direct responses to continuously upgrading consumption demand.

Construction of Evaluation System for Consumption-Led Economy

On basis of the previous research, this paper constructs an evaluation system to quantitatively measure the degree of consumption-ledness of China’s provincial economies. The construction of the system needs to follow two principles: firstly, the selected indicators must comprehensively reflect the connotation of a consumption-led economy, with complementary and correlated indicators at the same level, and compatibility among indicators at different levels; secondly, the selected indicators must be measurable, with data availability to ensure operationality, facilitating the convenient acquisition of necessary data for measurement and comparison in actual research. Guided by these two principles, it will help establish a systematic and quantifiable, comprehensive, and operational indicator system for a consumption-led economy, providing a powerful quantitative tool for in-depth research on the development of consumption-led economies in China’s provincial economies.

Following the above principles, based on the connotation and main features of a consumption-led economy, we construct the consumption-led economic indicator system shown in Table 1.

Table 1.

Evaluation Indicators for Consumption-Led Economy.

Primary indicators	Secondary indicators	Unit
Economic Autonomy	1. Per Capita GDP	10,000 RMB
	2. Economic Scale	100 million RMB
	3. Economic Growth Stability	%
	4. Proportion of Labor Compensation to Regional GDP	%
	5. Investment Efficiency	%
Demand Structure	6. Capital Formation Rate	%
	7. Household Consumption Rate	%
	8. Contribution of Consumption Expenditure to GDP Growth	%
	9. Dependence on Foreign Trade	%
Consumption Level	10. Total Retail Sales of Consumer Goods	100 million RMB
Consumption Level	11. Per Capita Consumption Level	RMB per person
Consumption Structure	12. Engel’s Coefficient	%
	13. Urban-Rural Income Gap	%
	14. Proportion of Cultural Consumption	%
	15. Proportion of Service Consumption	%
Consumption Environment	16. Infrastructure	-
	17. Medical and Health Conditions	-
	18. Cultural Quality of Consumers: Number of University Students per Ten Thousand People	People
	19. Social Security: Number of Basic Medical Insurance Participants	Ten thousand people
	20. Environmental Policy: Proportion of Public Service Expenditure to Local Financial Expenditure	%
	21. Science and Technology: Proportion of Scientific and Technological Expenditure to Local Financial Expenditure	%

The evaluation system selects five factors including economic autonomy, demand structure, consumption level, consumption structure, and consumption environment as primary indicators to assess the development status and influencing factors of a consumption-led economy from different perspectives. Economic autonomy reflects the level of independence and autonomy in economic development. Demand structure measures the relative contributions of consumption, investment, and exports to economic growth, reflecting changes in the economic growth model and demand pattern. Consumption level measures the level of residents’ consumption capacity and consumption behavior, directly affecting the traction effect of consumption on economic growth. Consumption structure reflects the proportion and trend of different consumption categories in total consumption, representing the optimization and upgrading level of economic structure. Consumption environment includes factors such as infrastructure, medical and health conditions, consumer culture, social security, environmental policies, and technological levels, influencing consumer experience and confidence.

In the secondary indicators, per capita GDP represents the stage of economic development, economic growth stability is represented by the absolute value of the difference in economic growth rates between adjacent years, investment efficiency is represented by the ratio of the growth rate of fixed asset investment to regional GDP, and the consumption rate is calculated by the ratio of household consumption expenditure calculated by the expenditure method to regional GDP, and the urban-rural income gap is represented by the ratio of urban residents’ average annual income to rural residents’ average annual income. Since the 16th to 21st individual indicators are not the final indicators and lack specific research data, substitutions and explanations are made indirectly to reflect their significance and connotation. Among them, the infrastructure under secondary indicators includes two tertiary indicators: road density and total postal business volume. Road density is represented by the ratio of expressway mileage to urban land area; medical and health conditions include the number of health technicians per ten thousand people and the number of medical institution beds per ten thousand people. Good medical conditions help reduce residents’ precautionary savings, while improved accessibility to healthcare resources can enhance households’ willingness to make major consumption expenditures. The data in this paper mainly come from various cities’ Statistical Yearbooks, and the specific indicator establishment and calculation methods refer to the establishment of Mao and Sun (2015), and the calculation method of investment efficiency is selected due to lack of data, referring to the calculation method proposed in Huang et al. (2022). Meanwhile, after referring to the literature of Lv and Zeng (2020), urban-rural income gap is added in the consumption structure. When the urban-rural income gap widens, wealth becomes increasingly concentrated among urban high-income groups with lower marginal propensity to consume, which suppresses overall consumption willingness and consequently negatively impacts the city’s evaluation score. Therefore, this indicator is classified as a negative-oriented metric. Additionally, the factor of science and technology is added in the consumption environment primary indicator to test the region’s innovation level. The greater the local support for science and technology, the more developed the digital infrastructure and the higher the rate of technological commercialization. Furthermore, technological advancement fosters the growth of tech-driven enterprises, promotes the opening of public data, and facilitates the flow of data elements, thereby optimizing resource allocation and stimulating consumption. Similarly, consumers in tech-oriented cities exhibit greater confidence in future consumption.

VHSD-EM Dynamic Evaluation Framework of Consumption-Led Economy

The Yangtze River Delta urban agglomeration is a crucial urban cluster in the eastern coastal region of China, consisting of six main metropolitan areas: the Shanghai metropolitan area, Hangzhou metropolitan area, Nanjing metropolitan area, Hefei metropolitan area, Suzhou-Wuxi-Changzhou metropolitan area, and Ningbo metropolitan area. Taking into account the representativeness, typicality, and feasibility of the selected cities, this study chooses six cities—Shanghai, Hangzhou, Nanjing, Hefei, Suzhou, and Ningbo—as our research objects. The sample selection adheres to three key criteria that authentically reflect the Yangtze River Economic Belt’s “core leadership with tiered coordination” development pattern: (1) The distinct variations in economic scale and development stages (Table 2) correspond perfectly with the region’s multi-level urban roles—ranging from Shanghai as a global city benchmark to emerging industrial centers like Hefei and Suzhou; (2) Each city’s consumption profile aligns with its functional orientation: Shanghai and Hangzhou serve as premium service consumption hubs with international characteristics; Suzhou and Hefei showcase a unique “manufacturing base plus consumption upgrade” development model; while Ningbo capitalizes on its export-driven economy; (3) All six cities are strategically located in the Yangtze Delta core area, representing both exemplary cases of high-quality development and successful implementations of the “ecological conservation with coordinated development” strategy. These cities span different geographical coordinates, exhibit various economic levels and industrial structures, and shape unique consumption environments and cultural characteristics, representing vivid snapshots of the development of each urban cluster.

Table 2.

Comparative Economic Indicators of Sample Cities (2022 年).

City	GDP (trillion RMB)	Per capita GDP (10,000 RMB)	Service sector share (%)	Urbanization rate (%)
Shanghai	4.4653	17.99	74.12	89.3
Nanjing	1.6908	17.8781	62.24	87.01
Suzhou	2.3958	18.6024	51.11	82.12
Hangzhou	1.8753	15.2588	68.19	84
Ningbo	1.5704	16.3911	50.36	78.9
Hefei	1.2013	12.5798	60.26	84.64

Furthermore, considering the continuous development stages of the consumption-led economy, the significant trend of consumption upgrading, the transformation of consumer demand towards quality and efficiency, and the emergence of new consumption formats, new data and information will be continuously supplemented and corrected. Complete, accurate, and obtainable data facilitate in-depth analysis and enrich the research perspective. Therefore, this study ultimately selects the period from 2015 to 2021 as the research time frame.

Building upon the foundation of evaluation systems, this paper further expands the methodology by proposing the Vertical and Horizontal Scatter Degree and Entropy Method (VHSD-EM) model for the weight determination of indicators. The original VHSD was introduced by Yi et al. (2016), aiming to better reflect the differences among evaluated objects across different time dimensions by incorporating the time factor into the process of determining indicator weights. Later, Jian et al. (2022) applied the VHSD-EM model to assess the high-quality development of the digital innovation and shipping industry in 11 China’s coastal provinces from 2010 to 2019. Jian et al. (2023) employed the random forest algorithm and VHSD-EM model to study the impact of artificial intelligence on wholesale and retail trade in the temporal and spatial dimensions. Tian and Tunio (2023) applied the VHSD-EM to assess the investment risk levels of foreign agricultural in Belt and Road countries from 2014 to 2021. However, this method has limitations as it relies on mathematical models to determine weight values through the construction of evaluation matrices, which may not adequately account for the information contained in each evaluation indicator. In response to this limitation, by integrating the advantages of dynamic and static methods, this model more accurately reflects the contribution of each indicator to the overall evaluation, resulting in more objective, comprehensive, and credible evaluation results. Additionally, it provides a better representation of the dynamic development trends of the consumption-led economy in the Yangtze River Delta urban agglomeration.

The specific steps of the improved VHSD-EM evaluation model are outlined as follows:

Step 1: Constructing the evaluation matrix. Assuming that the evaluation system spans from time $t (t = 1, \dots T)$ , the evaluated objects are denoted as $x (x = 1, \dots h)$ , and the indicators are denoted as $c (c = 1, \dots l)$ , collecting the values of each indicator for each year allows us to obtain an evaluation matrix $A_{t}$ for each year:

A_{t} = [\begin{matrix} a_{11} & a_{12} & a_{13} & \dots & a_{1 l} \\ a_{21} & a_{22} & a_{23} & \dots & a_{2 l} \\ a_{31} & a_{32} & a_{33} & \dots & a_{3 l} \\ \dots & \dots & \dots & \dots & \dots \\ a_{h 1} & a_{h 2} & a_{h 3} & \dots & a_{hl} \end{matrix}], t = 1, 2, \dots, T

(1)

where $a_{ij}$ represents the evaluation value of the evaluated object $x_{i}$ for the indicator $c_{i}$ , $i = 1, 2, \dots h; j = 1, 2, \dots, l .$

Step 2: Normalization of Indicator Data. Since the evaluation indicators may have different scales, it is necessary to standardize the data of all basic indicators to ensure comparability and scientificity of the evaluation results. Specifically, the range transformation method is used to calculate the standardized values of each indicator.

For positively oriented indicators:

y_{ij} = \frac{x_{ij} - N_{ij}}{M_{ij} - N_{ij}}, i = 1, 2, \dots, m; j = 1, 2, \dots, n

(2)

For negatively oriented indicators:

y_{ij} = \frac{M_{ij} - x_{ij}}{M_{ij} - N_{ij}}, i = 1, 2, \dots, m; j = 1, 2, \dots, n

(3)

where $y_{ij}$ represents the standardized value of each indicator after unit transformation, $N_{ij}$ is the minimum value in the column where the original data is located, $M_{ij}$ is the maximum value in the column where the original data is located. After standardization, the indicator $a_{ij}$ will have values ranging between 0 and 1.

Step 3: Indicator weights based VHSD method. Based on the data matrices for each year, their transpose matrices are first calculated and denoted as $A_{t}^{T}$ . According to the principle of the VHSD method, to maximize the representation of differences between samples, the method of total sum of squared deviations is adopted here.

σ^{2} = \sum_{t = 1}^{n} \sum_{i = 1}^{h} {(A_{it} - \bar{A_{it}})}^{2} = δ^{'} \sum_{t = 1}^{n} H_{t} δ = δ^{'} H δ

(4)

where $A_{it}$ represents the comprehensive evaluation value of the $i$ -th evaluated object in the t-th year; $\bar{A_{it}}$ represents the mean of the evaluation values of the i th evaluated object in the t-th year; δ denotes the indicator weight matrix, and $δ = {(δ_{1}, δ_{2}, \dots δ_{l})}^{T}$ . $H = \sum_{t = 1}^{n} H_{t}$ is a symmetric matrix, where $H_{t} = A_{t}^{'} A_{t}, t = 1, 2, \dots n$ . Under the condition of $δ^{'} δ = 1$ , when selecting the eigenvector corresponding to the maximum eigenvalue of matrix H, $σ^{2}$ attains its maximum value.

Thus, extracting the eigenvector corresponding to the maximum eigenvalue of matrix H, denoted as γ, and normalizing this eigenvector yields the weight vector $δ_{j}$ :

δ_{j} = \frac{γ_{j}}{\sum_{j = 1}^{l} γ_{j}}

(5)

Step 4: Utilization of entropy method for indicator weights. According to the entropy method, the specific steps for calculating the weights of indicators involve determining the degree of variation among the data for each indicator and each year, denoted as $v_{ijt}$ :

v_{ijt} = \frac{a_{ijt}}{\sum_{i = 1}^{h} a_{ijt}}

(6)

where $v_{ijt}$ represents the characteristic weight of the j-th indicator of the -th evaluated object in the t-th year.

Based on the variation degree of each indicator for each year, we can obtain the entropy value $e_{jt}$ , which represents the entropy of each indicator for each year.

e_{jt} = - \frac{1}{\ln (h)} \sum_{i = 1}^{h} v_{ijt} \ln (v_{ijt})

(7)

The coefficient of variation for each indicator is denoted as $d_{jt}$ , which is calculated as:

d_{jt} = 1 - e_{jt}

(8)

In Eq. (8), when $d_{jt}$ is larger, it indicates that the indicator j contains more information about the evaluated objects, and therefore should be assigned a larger weight, making the indicator more important. Hence, the entropy weight of the indicator is determined as:

μ_{jt} = \frac{d_{jt}}{\sum_{j = 1}^{l} d_{jt}}

(9)

Step 5: Calculating the Composite Weight of Indicators. Based on the VHSD indicator weight $δ_{j}$ and the entropy method indicator weight $μ_{jt}$ as the foundation, a combined approach is employed to construct the composite weight $φ_{jt}$ for each indicator:

φ_{jt} = \frac{δ_{j} + μ_{jt}}{2}, j = 1, 2, \dots, l

(10)

Step 6: The comprehensive score $P_{i t}$ for the i-th region in the t-th year is calculated as follows:

P_{it} = \sum_{j = 1}^{l} a_{ij} φ_{jt}

(11)

Due to limitations in the article length, Table 3 presents the results of the weight calculations for the indicators in 2015 and 2021.

Table 3.

Weight of Indicators for Consumer-Led Economic Development.

Primary indicators	Secondary indicators	Weight (2015)	Weight (2021)
Economic autonomy	1. Economic Development Stage	0.0327	0.0533
	2. Economic Scale	0.0327	0.0535
	3. Economic Growth Stability	0.0844	0.0486
	4. Proportion of Labor Compensation to Regional GDP	0.0571	0.0465
	5. Investment Efficiency	0.0620	0.0374
Demand Structure	6. Capital Formation Rate	0.0657	0.0375
	7. Household Consumption Rate	0.0826	0.0474
	8. Contribution of Consumption Expenditure to GDP Growth	0.0780	0.0531
	9. Dependence on Foreign Trade	0.0565	0.0471
Consumption Level	10. Total Retail Sales of Consumer Goods	0.0327	0.0535
Consumption Level	11 Per Capita Consumption Level	0.0327	0.0535
Consumption Structure	12. Engel’s Coefficient	0.0359	0.0463
	13. Urban-rural Income Gap	0.0357	0.0535
	14. Proportion of Cultural Consumption	0.0601	0.0411
	15. Proportion of Service Consumption	0.0431	0.0457
Consumption Environment	16. Infrastructure	0.0356	0.0428
	17. Healthcare Conditions	0.0327	0.0508
	18. Consumer Cultural Quality	0.0287	0.0507
	19 .Social Security	0.0464	0.0507
	20. Environmental Policies	0.0346	0.0347
	21. Science and Technology	0.0302	0.0525

Simultaneously, the weights of each secondary indicator are summed up to calculate the total weights of the primary indicators for 2015 and 2021, representing the contribution of the five factors to the final score of the consumer-led economic model. Through dynamic analysis of the contribution of primary indicators, we can gain insights into the factors influencing economic growth in various cities and understand the changing trends in the impact of these factors over time. Overall, the impact of consumption level, consumption structure, and consumption environment on the consumer-led economy has shown an increasing trend, while the influence of economic autonomy and demand structure has slightly decreased. The factor with the highest contribution is the consumption environment, accounting for 20.81% and 28.21% in 2015 and 2021, respectively. The lowest contribution comes from consumption level, which was only 6.53% in 2015 and increased to 10.71% in 2021. Meanwhile, the demand structure exhibits the largest fluctuation, decreasing from 28.27% in 2015 to 18.50% in 2021. On the other hand, the fluctuation in consumption structure is minimal, increasing from 17.48% in 2015 to 18.66% in 2021. The contribution and fluctuation of different factors vary, indicating significant changes in the development environment of China’s consumer-led economy over the past 7 years.

The contribution of economic autonomy decreased from 26.89% in 2015 to 23.93% in 2021, indicating its importance in maintaining the foundation and stability of the consumer-led economy. The decline in the contribution of economic autonomy is largely due to increased uncertainty in the international economic environment. With rapid globalization, trade frictions such as the China-US trade war and geopolitical tensions may also have an impact on economic autonomy.

The contribution of demand structure decreased from 28.27% in 2015 to 18.50% in 2021, indicating a gradual weakening of the influence of demand structure on the consumer-led economy. As China’s economy shifts from high-speed growth to high-quality development, changes in consumption structure and demand patterns are also occurring. The transition from an export- and investment-driven growth model to a consumption-led economic model is underway. Although demand structure still plays an important role in the consumer economy, its decreasing relative contribution reflects the new trend of economic structural adjustment in China. Additionally, factors such as global trade frictions, rising protectionism, and slowing global economic growth have led to a decrease in China’s dependence on foreign trade, reducing the impact of external demand on domestic demand structure and emphasizing the importance of domestic demand stimulation.

The contribution of consumption level increased from 6.53% in 2015 to 10.71% in 2021, indicating a strengthened contribution of consumption level to economic growth and a more positive role in driving economic growth. As China’s economy continues to develop and people’s living standards improve, the consumer market continues to expand, with steady growth in total retail sales of consumer goods. At the same time, the increase in per capita income and changes in consumption attitudes have led consumers to demand higher quality and services, enhancing the activity of the consumer market. This change is directly related to the transformation of the economic development model, especially in the first three quarters of 2023, when the contribution of final consumption expenditure to economic growth reached 83.2%, highlighting the dominant position of consumption in the economy. With a vast domestic market and strong consumption capacity, China’s market has witnessed the rise of emerging demands such as health, environmental protection, and intelligence, providing strong support for the development of a consumption-led economy.

The contribution of consumption environment increased from 20.82% in 2015 to 28.21% in 2021, reflecting the improvement in the consumption environment and the strengthening of government efforts to protect consumer rights, thereby further promoting the development of a consumption-driven economy. In recent years, continuous improvement in infrastructure construction, such as increased transportation convenience and improved logistics efficiency, has provided consumers with a better shopping environment. Meanwhile, improvements in medical and health conditions, enhancement of consumer cultural literacy, strengthening of environmental policies, and advances in science and technology have all supported the improvement of the consumption environment, promoting the popularization of green and intelligent consumption concepts and enhancing consumer experience and satisfaction.

In conclusion, economic autonomy, demand structure, consumption level, consumption structure, and consumption environment are important factors shaping the consumer-led economy. In recent years, the contribution and trend changes of these factors reflect the development status of the consumer economy. However, these changes are influenced by various factors, including the international economic environment, policy adjustments, and technological progress. Therefore, it is necessary to continue monitoring the changes in these factors and take effective measures to respond to challenges, promoting the new pattern of China’s economic development.

The Evaluation of Consumer-Led Economic Development in Major Cities of the Yangtze River Delta Region

Before conducting empirical calculations, it is necessary to verify the robustness of the VHSD-EM combined model, ensuring that the evaluation results obtained from both the VHSD and EM are consistent for their combined use. First, based on the weights derived from the VHSD and EM, calculate the comprehensive evaluation values $r_{it}$ and $y_{it}$ for the six cities from 2015 to 2021:

r_{it} = \sum_{j = 1}^{l} a_{ij} δ_{j}

(12)

y_{it} = \sum_{j = 1}^{l} a_{ij} μ_{jt}

(13)

Then, using the Spearman rank correlation coefficient, the results obtained from the VHSD and EM methods are tested separately, shown in Table 4. It can be observed that the coefficients of the evaluation values for each year are all greater than 0.8, and they are statistically significant at the 1% level. This indicates that the two methods exhibit good consistency, affirming the reliability of the combined “VHSD-EM” model weights proposed in this study.

Table 4.

Spearman Rank Correlation Test Results.

Year	2015	2016	2017	2018	2019	2020	2021
Coefficient	0.821***	0.893***	1***	0.964***	0.964***	0.853***	0.857***

Note. *** indicates significance at the 1% level, indicating that the correlation coefficient is significantly different from 0.

On this basis, the comprehensive evaluation and rankings of the consumption-led economy in the six major cities in the Yangtze River Delta from 2015 to 2021 were calculated according to Eq. (11). The results are shown in Table 5 and Figure 1.

Table 5.

Consumer-Led Economic Ranking of Cities from 2015 to 2021.

City	2015	Ranking	2016	Ranking	2017	Ranking	2018	Ranking	2019	Ranking	2020	Ranking	2021	Ranking
Hangzhou	3.212	5	4.040	5	4.548	4	5.198	3	6.916	2	5.515	5	7.785	2
Hefei	5.154	1	5.043	2	5.719	1	4.662	6	6.153	6	5.600	4	7.381	4
Nanjing	2.132	6	3.402	6	3.239	6	5.015	4	6.809	3	5.439	6	6.573	6
Ningbo	4.078	2	4.407	3	3.534	5	4.977	5	6.379	5	5.939	1	7.232	5
Shanghai	3.679	4	4.194	4	4.856	3	5.991	2	7.280	1	5.933	2	7.830	1
Suzhou	3.820	3	5.290	1	5.301	2	6.301	1	6.735	4	5.652	3	7.404	3

Figure 1.

Trend of the dominant economic scores of each city.

The comprehensive scores of the consumption-led economy in the six cities in the Yangtze River Delta range from 2.132 to 7.83 over the years. Referring to the division of comprehensive scores for consumption-led economies by Mao and Sun (2015), combined with Rostow’s six-stage division of economic development stages, we can divide the scores into four stages, as shown in Table 6.

Table 6.

Division of Scores for Consumption-Led Economies.

Score	Characteristic
2.0~4.5	Clearly investment-led, with economic development largely driven by investment
4.6~5.8	Domestic demand primarily driven by investment
5.9~7.0	Domestic demand primarily driven by consumption, indicating a gradual shift towards consumption-driven economic structure
7.0~10.0	Economic growth exhibits prominent characteristics of consumption-led development

It is observed that the scores changes from 4.6 to 7.0, generally exhibiting the characteristics of domestic demand-led economies, transitioning from investment-led to consumption-led. In 2021, the mean of the comprehensive scores for consumption-led economies in the Yangtze River Delta region of China was 7.37. This indicates that the economies of major coastal cities in eastern China are in a high-mass consumption stage, with the overall economic growth pattern being consumption-led. This aligns with the current economic development status of coastal cities in eastern China. This trend reflects a transition in the economic development of the Yangtze River Delta region from a stage primarily reliant on investment and exports to a consumption-led model. As one of the three drivers, the rising contribution rate of consumption to economic growth signifies a greater emphasis on meeting domestic demand and highlighting domestic consumption as a key driver of economic growth.

Below is a detailed analysis of the specific situation of consumption-led economic development in six cities:

Shanghai and Hangzhou lead the way. Shanghai and Hangzhou share a similar overall trend. From 2015, both cities had comprehensive scores of 3.6 and 3.2, respectively, indicating an economy still dominated by investment. By 2021, both cities had surpassed a comprehensive score of 7.5, ranking first and second respectively, indicating a consumption-led economic growth model. The reasons behind this shift are manifold, particularly noteworthy is the substantial investment by the government in infrastructure construction. In 2015, the Yangtze River Delta region actively developed new cities in coastal development zones, with Hangzhou investing 135.5 billion yuan in infrastructure and Shanghai investing 142.508 billion yuan. By 2021, Shanghai and Hangzhou had made significant progress in digital economy and technological innovation, especially with Hangzhou’s flourishing internet startups and e-commerce industry, which provided more innovative products and services to the consumer market.

Hefei upholds technology investment as the driver of economic development. For a long time, Hefei has maintained a consistently high score in its consumption-led economy, showing an overall upward trend. In 2015, Hefei ranked first in urban rankings, with a consumption-led economic score of 5.2, indicating a stage dominated by domestic demand. By 2021, Hefei had dropped to fourth place in urban rankings, but its consumption-led economic score exceeded 7.0, officially entering the consumption-led stage.

As one of China’s important science and education centers, Hefei has inherent advantages in forming industrial clusters centered around high-tech industries. Meanwhile, the Hefei municipal government, with a forward-looking vision, targeted the semiconductor industry, taking the lead in laying out the integrated circuit industry. To overcome challenges such as insufficient innovation and difficulty in transforming and upgrading pillar industries, the government has creatively established a diversified and three-dimensional investment and financing system. This system combines social capital with industrialization special funds to provide funding and policy support for technology companies at different stages, thus promoting the sustainable development of enterprises.

Ningbo actively responds to major changes. Ningbo’s ranking in terms of its consumption-led economy fluctuated significantly from 2015 to 2021. In 2015, Ningbo ranked second in urban rankings with a consumption-led economic score of 4.1, indicating a stage dominated by investment. By 2020, Ningbo had risen to first place in urban rankings with a consumption-led economic score of 6.0, indicating a stage dominated by domestic demand. In 2021, Ningbo’s consumption-led economic score exceeded 7.0, entering the consumption-led stage.

As an important port city in the Yangtze River Delta region, Ningbo’s economic dependence on exports exceeds 50%. When global foreign trade suffered a major setback, Ningbo’s export trade achieved “contrary growth.” This achievement relied on the timely initiation of the “expanding consumption” strategy by the government, expanding the domestic market, fully implementing pro-business policies, and promoting the optimization of industrial structure. This is also the reason why Ningbo was able to rise to the top of the comprehensive score rankings in 2020. Furthermore, Ningbo’s steadily rising consumption-led economic score is attributed to the integration of high-tech applications. In recent years, new drivers such as emerging industries, new technologies, and new formats have vigorously promoted Ningbo’s economy to achieve stable and high-quality development. The continuous growth of fixed asset investment and the steady expansion of the consumer market have laid a solid foundation for economic development.

Suzhou: Years of stable development. Suzhou’s ranking in terms of its consumption-led economy remained relatively stable from 2015 to 2021. In 2015, Suzhou ranked third in urban rankings with a consumption-led economic score of 3.8, indicating a stage dominated by investment. By 2021, Suzhou maintained its third position in urban rankings with a consumption-led economic score of 7.4, indicating a consumption-led stage. In 2015, faced with the outflow of foreign investment, the Suzhou municipal government constructively proposed the “seeking progress while maintaining stability, and grasping with both hands” strategy. This strategy aimed to construct a new development pattern driven by both domestic and foreign capital, with mutual promotion of domestic and international circulation, to achieve stable economic growth and structural adjustment. Relying on its superior business environment, Suzhou gradually shifted its development focus to supporting and nurturing local private enterprises. It continuously introduced high-tech industries to fill industry gaps, promoted the intelligent transformation of traditional industrial enterprises, and expanded effective assets. By utilizing the city’s rich educational resources, talent pool, and information resources, Suzhou built a multi-level enterprise innovation system and formed innovative industrial clusters.

However, in recent years, with changes in the global economy and adjustments in the domestic economic structure, Suzhou has gradually encountered significant economic development issues, such as a lack of strong industrial economy and absence of brands and technologies. In order to maintain high-quality development, Suzhou is actively promoting industrial transformation and upgrading, aiming at new tracks in the digital economy, and building industrial innovation clusters. In 2020, Suzhou’s regional GDP also successfully reached a new stage of “2 trillion yuan,” ushering in a new era of development after surpassing the “two trillion” mark.

The research findings reveal that the Yangtze River Delta urban agglomeration is undergoing an overall transition toward a consumption-driven economic model. However, deeper analysis indicates that cities of different sizes and development stages exhibit significant heterogeneous characteristics during this transformation process.

As the regional economic center, Shanghai, leveraging its internationalized consumption environment and high-end service industry advantages, ranked first with a comprehensive score of 7.83 in 2021. Hangzhou, supported by its thriving digital economy and new retail formats, closely followed with a score of 7.79. Hefei, through strategic focus on high-tech industries such as semiconductors, has developed a distinctive consumption model centered on tech talent, though the urban-rural consumption gap remains notable. As a major port city, Ningbo actively promoted a “domestic sales substitution for exports” strategy amid external environmental changes, significantly increasing consumption’s contribution to economic growth. Suzhou, by fostering collaboration between foreign-funded enterprises and local manufacturing, cultivated a transformation path characterized by producer services consumption. In contrast, Nanjing, constrained by its higher proportion of traditional industries, lagged in consumption-driven transition, scoring only 6.57 in 2021.

Conclusion and Recommendations

Based on the determination of the connotation and main characteristics of a consumption-led economy, this paper constructs an evaluation system for the development of consumption-led economies in the Yangtze River Delta’s main cities. Using the VHSD-EM model, it determines the weights of each indicator and evaluates the level of consumption-led economic development in the six cities of the Yangtze River Delta from 2015 to 2021, analyzing in-depth the temporal and spatial differences in the development of consumption-led economies.

The study found that the overall trend of urban cluster development in the Yangtze River Delta is towards a consumption-led economy, and localities should actively guide consumption development. In 2021, the comprehensive score of consumption-related indicators in the central cities of the Yangtze River Delta urban cluster reached 7.37, indicating that the economy of this urban cluster is moving towards high-quality development. The overall economic growth pattern is characterized by domestic demand-led with consumption as the main driver. This conclusion aligns with the actual situation of economic growth in the Yangtze River Delta urban cluster, known for its dense urban population, developed economic system, and strong industrial base. The improvement in the comprehensive score indicates that the region’s economy is gradually adjusting from a traditional production and investment-oriented model to one that places more emphasis on domestic demand and consumption.

Furthermore, among the six major cities, Shanghai is currently the most mature city in terms of the consumption-led model, followed by Hangzhou, Suzhou, Hefei, Ningbo, and Nanjing. Hangzhou and Ningbo are both located in Zhejiang Province, while Suzhou and Nanjing are in Jiangsu Province. However, there are significant differences in the comprehensive scores of consumption-led models between these cities, indicating substantial disparities in development stages even within the same provincial domain. Meanwhile, compared to Hangzhou, Ningbo, which has a higher dependence on foreign trade, also faces issues of insufficient exploration of the domestic market in developing a consumption-led economy.

Based on the above analysis, this paper proposes the following targeted recommendations: (1) Consumer-oriented and high-quality development. Emphasize the foundational role of consumption in economic growth and focus on high-quality development and efficient investment. Especially, Shanghai, as an economic leader, should further strengthen consumer guidance, focus on high-quality development, by optimizing the consumption environment and social welfare systems, we can unlock residents’ consumption potential to form a virtuous cycle between consumption and economic growth; (2) Differentiated Development. Medium-sized cities should neither simply replicate the models of developed regions nor passively wait for market evolution, but proactively forge a consumption-driven development path suited to their actual conditions. Consumption-driven transformation must be rooted in local characteristics—manufacturing cities can focus on cultivating producer services consumption, while science and education-oriented cities can develop consumption by tech talent, avoiding homogenized competition. At the same time, emphasis should be placed on the synergistic advancement of consumption upgrading and industrial transformation to create a virtuous cycle; (3) Social Security. Improve the social security mechanism and strengthen the formulation and implementation of social security policies, thereby reducing residents’ precautionary savings, unleashing the consumption potential of low- and middle-income groups, and ultimately expanding domestic demand to promote economic development; (4) Innovation-driven and emerging technology development. Encourage research and development innovation, prioritize the development of emerging technology products, vigorously foster the emergence of new consumption growth points, form a virtuous cycle of new consumption leading to new supply and new supply creating new demand, promote the transformation and upgrading of traditional manufacturing industries, and drive economic growth; (5) Reducing external dependence and achieving economic balance. To promote the development of a consumption-led economy, cities can reduce their dependence on foreign investment and foreign trade, optimize the conversion of economic growth drivers, achieve mutual promotion of domestic and international dual cycles. Ningbo can deepen the transformation from foreign trade to domestic sales, accelerate the shift of goods to the domestic market through cooperation with e-commerce platforms, and increase the share of the domestic demand market.

Limitations and Future Research Directions

(1) Data granularity and contextual factors: The study does not delve into detailed consumption data, regional disparities, or post-pandemic dynamics in China. Future research could incorporate the latest consumption statistics, regional economic divergence patterns, and policy adjustments in the post-COVID era to enhance empirical analysis, providing a more comprehensive assessment of China’s transition to consumption-driven growth and its associated challenges.

(2) Measurement constraints: Due to data limitations, we proxied investment efficiency using the ratio of fixed-asset investment growth to regional GDP growth, rather than the incremental capital-output ratio (ICOR). This alternative metric may not fully capture productivity improvements, suggesting a need for more refined indicators in subsequent studies.

(3) Aggregation bias: While the analysis focuses on macro-level city data, it omits firm- and household-level insights. Future work could integrate microdata to uncover heterogeneous effects across economic agents.

(4) Omitted drivers: Critical factors such as industrial upgrading, FDI inflows, labor mobility, and technology adoption remain underexplored. Without examining these dimensions, it remains unclear whether observed consumption patterns stem from policy interventions, endogenous economic conditions, or broader macroeconomic trends.

Footnotes

ORCID iDs

Jinxiu Yi

Shouzhen Zeng

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by the by the project of Economic Forecasting and Policy Simulation Laboratory, Zhejiang Gongshang University (No. 2024SYS034), Zhejiang Provincial Natural Science Foundation of China (No. LMS25G010002), Research Project on Education Science Planning of Ningbo City (No. 2025YGH002) and The Philosophy and Social Science Fundation of Colleges and Universities in Jiangsu Province (No. 2022SJYB1021).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

References

Arellano

Blundell

Bonhomme

(2017). Earnings and consumption dynamics: A nonlinear panel data framework. Econometrica, 85(3), 693–734.

Chenery

H. B.

Syrquin

(1975). Patterns of Development, 1950–1970. New York: Oxford University Press for the World Bank.

Hong

Y. X.

(2013). Consumption demand, consumption power, consumption economy and economic growth. China Economic Studies, 1, 3–8.

Huang

Chen

Deng

(2022). How does digital economy affect China’s consumption-led economic growth level. Journal of Shanxi University of Finance and Economics, 44(4), 69–83.

Jian

Guo

(2022). Research on the impact of digital innovation driving the high-quality development of the shipping industry. Sustainability, 14(8), 4648.

Jian

Guo

(2023). Effect of artificial intelligence on the development of China’s wholesale and retail trade. Sustainability, 15(3), 10524.

Laukka

Rantakokko

Suhonen

(2019). Consumer-led health-related online sources and their impact on consumers: An integrative review of the literature. Health Informatics Journal, 25(2), 247–266.

Liu

D. H.

Zhu

G. L.

(2021). An analysis of the distribution characteristics of the pull effect of Chinese resident consumption on economic growth. Contemporary Economy & Management, 43(4), 8–12.

Z. K.

Zeng

(2020). The effect of urban and rural income inequality on consumption structure—An empirical study based on the perspective of limited rational decision. Journal of Xiangtan University, 44(6), 74–79.

10.

Mao

Z. G.

Sun

(2015). China provincial economic growth mode valuation: Analysis of consumption-led index system. Statistical Research, 32(9), 68–75.

11.

Shi

L. Y.

Zhang

C. Y.

H. G.

(2021). Consumption upgrading, consumption rate changing and economic development divergence—An empirical analysis based on threshold model and mechanism. China Business and Market, 35(9), 74–85.

12.

Sommer

(2007). Habit formation and aggregate consumption dynamics. The B.E. Journal of Macroeconomics, 7(1), 1444.

13.

Sun

(2015). Consumption leading powers: Characteristics, measures and policies. Journal of Social Sciences, 10, 36–46.

14.

Sun

Mao

Z. G.

Gui

H. Q.

(2017). China’s economic growth pattern evolution and regional difference. Inquiry into Economic Issues, 6, 30–38.

15.

Tian

Tunio

F. H.

(2023). Assessing financial risks of foreign agricultural investment in belt and road countries: A risk index approach and VHSD-EM model analysis. PloS One, 18(12), e0293146.

16.

Waldfogel

(2003). Preference externalities: An empirical study of who benefits whom in differentiated-product markets. RAND Journal of Economics, 34(3), 557–568.

17.

Wen

Yao

(2002). A positive analysis and the policy choices of the relationship between residents consumption and economy increase. Academic Forum, 4, 55–57.

18.

Yang

Sicular

Gustafsson

(2024). China’s prosperous middle class and consumption-led economic growth: Lessons from household survey data. The China Quarterly, 258, 479–494.

19.

Zhou

Guo

(2016). A dynamic comprehensive evaluation method embodying development tendency. Operations Research and Management Science, 25(6), 175.

20.