Preamble

Zhetong Li¹, Yanshuo Huang², Xiaoxian Peng³, Qian Chen⁴, and Ling She⁵

¹Hunan University of Finance and Economics, Changsha, 410205, Hunan, China

Keywords: Smart pension policy; Aging population; Economic growth; Threshold model; DID model; Mediation effect model

According to data from the National Bureau of Statistics, as of 2023, China's elderly population aged 60 and above has exceeded 296 million, accounting for 21.1% of the total population. This highlights the severity of China's population aging challenge. Against this macro backdrop, the traditional elderly care service model has shown clear limitations. As the number of elderly individuals continues to rise, conventional approaches to elderly care face unprecedented pressure and challenges, testing not only the social welfare system but also compelling us to seek innovative technical solutions and service models to fill the supply gap. Consequently, the smart elderly care industry policy has emerged, leveraging advanced technologies to enhance quality of life for older adults. This paper examines the "silver-haired economy" under smart elderly care industry policy interventions, comprehensively analyzing how these external and internal factors influence economic growth.

Based on China's provincial panel data from 2008 to 2023, this study employs multi-dimensional empirical methods to explore the impact mechanisms of smart pension policies on China's economy, focusing on the mediating effects of capital investment, labor efficiency, and consumption structure. First, the difference-in-differences (DID) method is used to evaluate the economic effects of the smart pension pilot policy, revealing that policy implementation increased the GDP growth rate of pilot areas by approximately 1.8 percentage points on average, demonstrating a significant positive impact on economic growth. Second, this study tests the mechanism at the provincial level using a mediation effect model, finding that smart pension policies promote domestic economic development through three pathways: capital investment, labor supply, and consumption structure. When the degree of urban aging is low, this effect may be weakened. Finally, a threshold model analyzes the heterogeneity of smart elderly care industry policies, showing that when a region experiences both population aging and rapid economic development, the positive impact of policies on economic development becomes more significant.

Research demonstrates that smart elderly care industry policies effectively promote national economic growth through a triple mechanism: promoting industrial structure upgrading, forcing technological innovation, and activating demand-side dynamics. The study recommends building a three-dimensional development system of "demand identification-technology empowerment-policy coordination" and implementing gradient industrial policies tailored to regional aging patterns and economic differences.

1. Introduction

According to National Bureau of Statistics data, as of 2023, China's elderly population aged 60 and above has exceeded 296 million, accounting for 21.1% of the total population. This underscores the seriousness of China's population aging problem. More notably, according to National Health Commission forecasts, by 2050 the proportion of elderly population will reach an astonishing 34.9%, meaning China will enter a deeply aging society. Faced with this irreversible demographic trend, the former Premier of the State Council clearly stated: "Population aging is a critical issue facing current economic and social development. We must adapt to this trend and promote comprehensive adjustment of all economic and social work."

Under this macro background, the traditional elderly care service model has demonstrated obvious limitations. As the elderly population continues to grow, conventional elderly care approaches face unprecedented pressures and challenges across multiple dimensions including health management, daily care, and security. This not only tests the social welfare system but also forces us to seek innovative technical solutions and service models to fill supply gaps.

Technological innovation therefore plays a vital role in addressing elderly care challenges. The smart elderly care industry leverages advanced technologies such as the Internet of Things, big data, and artificial intelligence to provide more intelligent and personalized health management, care services, and home security protection for older adults. These innovative technologies can not only improve quality of life for the elderly but also enhance service efficiency and reduce social burden. However, the development of the smart elderly care industry faces not only bottlenecks in internal technological innovation but is also affected by external factors including policies, economic environment, and social acceptance. Therefore, comprehensive analysis of these external and internal factors is of great significance for understanding the smart elderly care industry and its impact on economic growth.

2.1. Policy Background

With accelerating population aging, China has entered a deeply aging society. According to National Bureau of Statistics data, by the end of 2024, the proportion of China's population aged 65 and above had climbed to 15.6%, with the elderly dependency ratio reaching 22.5%. Under the dual pressures of declining fertility rates and increasing life expectancy, the traditional family-based elderly care model faces structural challenges. The government-led institutional response system has been systematically constructed since the early 21st century: in 2011, the 12th Five-Year Plan for the Development of China's Aging Cause included intelligent elderly care in the policy agenda for the first time, marking the strategic start of digital transformation in elderly care services; in 2017, the Action Plan for the Development of Smart Healthy Elderly Care Industry (2017-2020), jointly issued by three ministries, clearly proposed a trinity development framework of "technology R&D, product services, and standard construction" to promote application scenarios of emerging technologies such as IoT and big data in elderly care. Notably, in 2019, "Several Opinions on Further Promoting the Development of Integrated Medical and Nursing Care" pioneered the integration of "medical and nursing care" policies, providing institutional guarantees for multi-modal development of smart elderly care services.

The evolution path of policy innovation shows significant top-level design characteristics: First, establishing a policy tone of multi-subject collaboration and co-construction, guiding social capital to participate in smart elderly care infrastructure construction through the PPP model; Second, building a vertically integrated policy support system. In 2021, the "14th Five-Year Plan for the Development of the National Aging Cause and Elderly Care Service System" clearly proposed to "improve the institutional framework for aging society governance," with 35 State Council departments establishing an inter-ministerial coordination mechanism to jointly promote aging-friendly transformation and digital inclusiveness policies; Third, forming a policy toolkit for supply-side reform. The Ministry of Finance established a special fund for smart, healthy elderly care application pilot demonstrations with over 5 billion yuan in investment, implementing tax incentives such as super deductions for R&D expenses. It is worth noting that institutional innovation by local governments is particularly valuable. Shanghai took the lead in systematically connecting the "long-term care insurance" system with data from smart wearable devices, while Shandong Province initially realized a spatial layout of "15-minute smart elderly care service circles" through community-embedded elderly care service centers. These policy practices represent not only an active response to the WHO's global "healthy aging" strategy but also a sample of smart pension system innovation with Chinese characteristics.

At the international governance level, China actively participates in the UN Decade of Healthy Aging (2021-2030) action plan, explicitly writing "application of digital technology in elderly care services" into its national commitment letter. Against this background, the State Council's "Overall Layout Plan for Digital China Construction" lists smart elderly care as a key development area, and the "Guiding Opinions on High-Quality Development of the Silver Economy" released in 2023 even proposes the strategic goal of building a national smart elderly care standard system. This policy framework not only embodies universal principles of global aging governance but also includes localized innovations based on China's institutional advantages. Its core essence is reconstructing the family-community-institution trinity elderly care service system through digital technology empowerment, providing institutional support for building a precise and personalized elderly care service supply network covering urban and rural areas.

2.2. Literature Review

2.2.1. Different Views on Smart Elderly Care Industry Policies

Most studies believe that smart pension policies can significantly promote economic growth, though regional heterogeneity is obvious. Zhao Qiang et al. (2021) found, based on provincial panel data from 2015 to 2020, that smart pension pilot policies contributed 0.8% to GDP growth in the eastern region, but the impact on central and western regions was not significant, with the mechanism lying in better technical infrastructure and higher policy implementation efficiency in the eastern region. Similarly, Chen et al. (2021) proved through the difference-in-differences (DID) model that policies indirectly stimulate regional economic growth by encouraging corporate R&D investment (with an average increase of 12%). However, some scholars point out policy effect lags. For example, Liu Yang (2022) believes that the commercialization cycle of technology-intensive elderly care services is long, short-term GDP growth may be overestimated, and long-term effects require further verification.

Academic circles generally recognize the structural reshaping effect of smart pension policies on the job market, but differ on impacts on low-skilled groups. On one hand, a National Development and Reform Commission (2023) report shows that the smart elderly care industry has driven demand for emerging occupations such as health managers and data analysts to grow at an average annual rate of 15%, partially offsetting labor surplus pressure in manufacturing. Wang Fang (2020) emphasized that the popularization of community smart elderly care service centers has created numerous localized service jobs. On the other hand, Li Hong (2022) found through micro-surveys that low-skilled elderly workers face employment exclusion risks due to insufficient technological adaptability (such as difficulties operating smart devices), and policies may aggravate the "digital divide" in the labor market.

Smart pension policies promote industrial structure tilting toward high-tech service industries and encourage cross-industry collaboration. Zhou Tao et al. (2023) pointed out that policies promote transformation of traditional manufacturing into "service-oriented manufacturing" by subsidizing intelligent assistive device manufacturers, such as integrating elderly care robot production with operation and maintenance services. Wu Xiaobo (2023) took Shanghai as an example to prove that smart elderly care policies have accelerated cross-border integration of medical, insurance, real estate and other industries, forming an "elderly care +" ecosystem, with the service industry proportion increasing by 3.2 percentage points.

2.2.2. Policy Influence Path and Mechanism

Scholars have analyzed the transmission path of policies on economic development from three perspectives: technological innovation, consumption upgrading, and regional coordination. Chen et al. (2021) found that for every 1% increase in government R&D tax relief for smart elderly care companies, the number of corporate patents increased by an average of 0.6%. Typical cases include rapid commercialization of AI nursing robots (for example, a Shenzhen company's product coverage rate increased to 20% within 3 years). Liu Yang (2022) emphasized that smart elderly care technology (such as telemedicine) has spread to other fields, driving innovation in related industries such as medical equipment and cloud computing, forming a "chain reaction." Hu Jing (2022) pointed out based on consumption data that smart elderly care policies have stimulated demand for mid-to-high-end services. For example, the annual growth rate of the smart home monitoring system market exceeds 25%, and consumption willingness among high-income elderly groups has increased significantly. However, Zhang Lei (2023) warned that if policies focus too much on the high-end market, they may ignore basic pension needs of low-income groups, leading to "structural imbalance" in consumption upgrading.

Zhao Qiang et al. (2021) proved that smart elderly care pilot cities generate approximately 0.3% positive spillover to surrounding cities' GDP within 200 kilometers through technology diffusion and capital flow. Cities represented by Shanghai and Hangzhou rely on policy support to form "smart elderly care industrial parks," attracting upstream and downstream enterprises to cluster and reducing collaboration costs (Wu Xiaobo, 2023).

2.2.3. Impact of Policies on Different Regions

Regional policy dividends are unevenly distributed. Ma Lin (2021) compared policy effects across eastern, central, and western regions, finding that due to stronger financial capacity and better digital foundations, the policy return rate in the eastern region is 2.5 times that of the western region, potentially aggravating regional economic disparities. However, some scholars advocate balancing regional resources through central fiscal transfer payments (Li Wei, 2021), though opponents believe this would weaken local innovation momentum (Zhang Wei, 2022).

Regional differences in aging make policy effects different. Eastern region: Policies play a significant role in stimulating GDP. Zhao Qiang et al. (2021) found that smart elderly care pilot policies increased average annual GDP growth in the Yangtze River Delta region by 0.5%, mainly due to expansion of technology-intensive service industries. Central and western regions: Policy effects are limited by infrastructure and human capital. For example, in Guizhou Province's smart elderly care equipment procurement project, 30% of equipment failed to go online due to insufficient network coverage (Zhang Wei, 2022).

In summary, researchers hold different views on the impact of smart elderly care industry policies on economic development. Analysis of policy paths and mechanisms mostly focuses on the enterprise level, while research on provincial administrative units remains relatively blank. Moreover, regional differences produce varying policy effects, with no unified and clear explanation in academic circles. The incremental contributions of this paper are: (1) Using provincial administrative unit panel data from 2008 to 2023, combined with smart elderly care industry pilot policies, to further explore the impact of smart elderly care industry policies at the provincial level on economic development; (2) Exploring the path and mechanism analysis of smart elderly care industry policies on economic development based on the provincial level; (3) Further exploring and verifying the heterogeneity of smart elderly care industry policies across different regions on economic development.

2.3. Theoretical Hypothesis

2.3.1. The Relationship Between Smart Elderly Care Industry Policy and Economic Development

Empirical research by most scholars shows that smart elderly care industry policies empower economic growth through multiple mechanisms. First, based on the theoretical framework of endogenous economic growth (Romer, 1990), smart elderly care policies form a digital technology innovation ecosystem by guiding R&D investment and building "1+N" technology application scenarios and standardization systems, promoting industrial application of aging-friendly smart devices and forming a compound innovation model of "silver economy + digital economy." For example, Japan's RIETI Research Institute estimates that every 1% increase in GDP of its smart elderly care industry can drive overall productivity to increase by 0.3-0.5 percentage points. Second, based on consumption upgrading theory, implementation of smart elderly care policies extends healthy life expectancy for the elderly, expands the consumption cycle, increases consumption of smart elderly care products, and forms intergenerational consumption synergy effects (children purchasing smart devices for parents). Research by the Chinese Academy of Social Sciences shows that the marginal contribution rate of smart elderly care consumption to economic growth is 0.6, verifying the applicability of consumption upgrade pull (Modigliani & Brumberg, 1954) in the silver-haired economy. Finally, based on new human capital theory (Becker, 1964), implementation of smart pension policies extends the healthy working life of the elderly population by 2-3 years (WHO, 2019), increases the human resource redevelopment rate for the elderly by 15%, and improves efficiency of intergenerational knowledge and skill transmission. According to U.S. BLS data, smart health technology has increased productivity of workers over 55 years old by 12% (BLS, 2020). Therefore, based on the above, the smart elderly care industry can positively promote economic growth through multiple transmission mechanisms.

H1: Smart pension policy has a positive effect on economic growth.

2.3.2. Capital Investment Perspective

Although population aging poses direct challenges to economic growth, it also creates historic opportunities for developing the smart elderly care industry. The smart elderly care industry forms a compound model of "silver economy + digital economy" through technological innovation (Charness & Boot, 2017), becoming a new driving force for economic transformation and upgrading while alleviating aging society pressures. This industry's development relies on synergy among technological progress, policy support, and market demand to promote formation of a complete industrial chain covering intelligent hardware, medical health, and digital services (China Electronics and Information Industry Development Research Institute, 2023). According to Ministry of Industry and Information Technology data from 2024, the smart health equipment market's average annual growth rate reaches 37%, with particularly significant efficiency improvements from technology penetration: in smart elderly care communities, intelligent nursing systems have increased the number of elderly served per capita by 87.5% (Shenzhen Civil Affairs Bureau, 2024), and telemedicine systems reduce home care costs by 40% (National Health Commission, 2023). This technological empowerment not only reconstructs elderly care service supply models but also promotes economic growth through labor resource reallocation.

Additionally, smart elderly care industry development has driven extension of related industrial chains, including smart hardware, medical health, financial services, and other fields, forming new economic growth points. For example, popularization of smart health devices has driven upgrading of basic industries such as chip manufacturing and sensors (China Electronics and Information Industry Development Research Institute, 2023), and construction of smart elderly care communities has a multiplier effect of 1:3.2 on related industries such as construction and logistics (State Council Development Research Center, 2022). Therefore, the smart elderly care industry not only alleviates social pressure from aging but also injects new momentum into economic growth. By promoting smart elderly care industry development, the negative impact of aging on economic growth can be offset to a certain extent, achieving sustainable economic development. Based on input-output theory (Leontief, 1936), we propose the following hypothesis:

H2: Smart elderly care industry policy indirectly promotes economic growth by promoting industrial capital investment (M1).

2.3.3. Labor Supply Perspective

Smart elderly care industry policy forces technological innovation and labor efficiency improvement (M2) by increasing labor costs and corporate operational pressures, thus positively impacting economic growth. The smart elderly care industry accelerates application of automation, artificial intelligence, and digital technologies. For example, manufacturing and service industries have significantly improved production efficiency and reduced dependence on traditional labor by introducing intelligent robots and big data analytics. This technological innovation not only compensates for labor supply shortages but also promotes optimization and sustainable development of economic structure. Additionally, smart elderly care industry policy has promoted structural reforms in the labor market, such as extending retirement age, encouraging elderly re-employment, and improving labor skills training. The human resource redevelopment rate for the elderly has increased by 15% (Elderly Talent Network, 2023). This structural optimization has significantly promoted efficiency of intergenerational knowledge and skill transmission (Bloom et al., 2018). Through policy support and technical subsidies, the government has further accelerated application of technological innovation in elderly care, medical care, and other fields, alleviating social pressure from aging. For example, popularization of telemedicine and smart health devices has not only improved medical service efficiency but also reduced medical costs, injecting new momentum into economic growth. Therefore, smart pension policy has played a positive role in promoting economic growth by forcing technological innovation and improving labor efficiency. Based on efficiency wage theory (Shapiro & Stiglitz, 1984), we propose the following hypothesis:

H3: The smart elderly care industry policy forces technological innovation, improves labor efficiency (M2), and indirectly promotes economic growth.

2.3.4. Perspective of Consumption Structure

The extension of life cycle theory (Modigliani & Brumberg, 1954) in the aging context shows that the prolonged consumption cycle of elderly groups forms "third age consumption" (Gilleard & Higgs, 2005). Data from the Chinese Society of Gerontology (2024) shows that average annual consumption expenditure on smart elderly care products reaches 12,000 yuan, driving the aging-friendly smart home market to grow by 210%. Additionally, elderly demand for leisure tourism, cultural entertainment, and lifelong education has also given birth to new consumer markets, further promoting economic growth. For example, rapid development of the tourism market for the elderly has led to development of many industries such as transportation, hospitality, and tourism services, while the rise of the education market for the elderly has promoted innovation in educational technology and cultural industries. Therefore, aging has promoted consumption structure upgrading and formation of new growth points by giving birth to the "silver-haired economy." This change in consumption structure not only alleviates the negative impact of aging on economic growth but also provides new impetus for sustainable economic development. Based on consumption stratification theory (Li Peilin, 2005), we propose the following hypothesis:

H4: The smart pension policy gives birth to the "silver economy" (M3), drives consumption structure upgrading, and indirectly promotes economic growth.

[TABLE:1]

3. Data and Methods

As of 2023, the smart elderly care policy has established five batches of pilot provincial administrative units. The first batch in 2017 includes Beijing, Tianjin, Shanghai, etc.; the second batch in 2018 includes Shanghai, Jiangsu, Zhejiang, etc.; the third batch in 2019; the fourth batch in 2020; and the fifth batch in 2022. Although there are five batches of pilots, the second and third batches have short implementation periods and are less affected by policies, while the fourth and fifth batches are almost consistent with the first batch of pilot cities. Therefore, this paper selects the first batch of pilots for research. The experimental areas are distributed across China's eastern, central, and western regions, with samples from other provincial administrative units serving as control groups.

After eliminating samples with missing values, we selected panel data from 31 provincial administrative divisions as samples, of which 21 provincial administrative divisions constitute the experimental groups. This yielded 496 observations between 2008 and 2023. All data are from the China Statistical Yearbook.

3.1. Data Description

3.1.1. Dependent Variable

The dependent variable in this paper is Gross Domestic Product (GDP). As an important indicator measuring regional economic development level, GDP reflects industrial, manufacturing, handicraft, and other regional economic development. There is an internal relationship between economic development and environmental governance. Therefore, GDP is used to measure the economic situation when exploring the impact of smart pension policy on economic growth and the mechanism through which the smart pension industry affects economic growth.

3.1.2. Independent Variable

The independent variables are the dummy variable (Policy) of smart elderly care industry policy effect and the intensity of smart elderly care industry policy effect. According to the "Smart Elderly Care Policy" mentioned in the "White Paper on the Development of China's Smart Elderly Care Industry" and the "2017-2019 (First Three Batches) Smart Healthy Elderly Care Application Pilot Demonstration Review List," this article sets the provincial administrative division unit index (treat) involved in the "Smart Elderly Care Policy" to 1 (experimental group = 1, control group = 0). The time effect variable (time) is then set according to year i when the "policy" began implementation, set to 0 before year i (excluding i) and 1 after year i. The independent variable (Policy) is obtained by multiplying the provincial administrative division unit index (treat) by the policy implementation year index (time). This paper measures the intensity of smart elderly care industry policy effect by the word frequency of "smart elderly care policy" on provincial government official websites, which is highly explanatory.

3.1.3. Control Variables

1. Fixed Assets (PPE). Fixed assets are an important driving force for economic growth. When studying the impact of "aging population structure" and "smart elderly care industry" on the economy, controlling fixed asset investment can eliminate interference from capital investment to more clearly observe the independent role of core variables.

2. Government Fiscal Expenditure (FE). Government expenditures (e.g., infrastructure, public services, subsidies) directly shape the economic environment, and controlling their size or structure can eliminate confounding effects of policy interventions on core variables.

3. Proportion of Tertiary Industry (PTI). The proportion of tertiary industry refers to the share of the service sector in the overall economic structure, vividly showing the important position and role of services in the national economy. The tertiary industry includes all service industries except industry and agriculture, such as finance, education, medical care, entertainment, information, etc. When the proportion of tertiary industry increases, it indicates regional development in services, high-tech industries, and the knowledge economy. Therefore, it should be used as a control variable when exploring the impact of smart elderly care industry on economic growth.

4. Population Aging Rate (ODR). Population aging rate is an important indicator measuring population aging structure in a region. It will be used as a control variable when exploring the "impact of smart elderly care industry on economic growth." This chapter uses the provincial-level population aging rate to measure regional population aging.

3.1.4. Mediating Variables

One mediating variable is capital investment (M1), measured by the market size of smart elderly care industry (TAM) and the number of registered smart elderly care enterprises in China (QTY). The second mediating variable is labor efficiency (M2), measured by labor research expenditure (RD) and labor productivity (EPRB). The third mediating variable is consumption structure (M3), measured by per capita medical care expenditure (CMC) and basic pension insurance fund expenditure (SSF).

3.2. Empirical Model

The difference-in-differences (DID) model is a design using observational data to simulate experimental research. Its basic idea is to divide survey samples into two groups: one group affected by policies (the experimental group, i.e., cities affected by smart elderly care), and the other group not affected by policies (the control group). First, calculate the change in an index in the experimental group before and after the policy, then calculate the change in the same index in the control group before and after the policy, and finally calculate the difference between these two variables to reflect the net policy impact (smart elderly care).

In this study, 31 provincial administrative divisions from 2008 to 2023 serve as the research objects, among which 21 provinces have implemented smart pension (experimental group = 21) and 10 provinces have not (control group = 10). Taking 2017 as the policy intervention point, the econometric model is established as follows:

Introducing control variables:

$$Y_{z} = \alpha + \beta + \mu + \gamma_{t} + \varepsilon_{z}$$

$$Y_{it} = \alpha + \beta D_{i} * T_{t} + \lambda C + \mu + \gamma_{t} + \varepsilon_{it}$$

$$\beta = [E(Y \mid D = 1, T = 1) - E(Y \mid D = 1, T = 0)] - [E(Y \mid D = 0, T = 1) - E(Y \mid D = 0, T = 0)]$$

Where Y _{it} is the outcome variable, D _{i} is the policy grouping dummy variable (taking 1 if city i implements the smart pension industry policy during the sample period, otherwise 0), T _{t} is the policy time dummy variable (1 after policy implementation, 0 before), D {i} * T is the interaction term between the two, C is the control variable, and are coefficients before the terms, and is the random error term. i and i are individual fixed effects and time fixed effects, respectively. By adding individual dummy variables and time dummy variables during regression, individual fixed effects and time fixed effects can be controlled.

3.3. Descriptive Statistics

According to Table 2 [TABLE:2], the time span is from 1995 to 2023 through the variable value range of the time primary key (year). The maximum GDP value is as high as 12941.7, while the minimum is 398.2, showing large regional differences in economic development levels. Economic indicators such as PPE and FE also show significant differences in numerical ranges.

[TABLE:2]

Before empirical regression, the data were trimmed at 1% to avoid influence from extreme values.

4.1. Parallel Trend Test

In this study, the basic assumption of the DID model is that before implementation of the "Smart Pension Industry Policy," there was no significant difference between the experimental and control groups in GDP dimensions. After introduction of the "Smart Pension Policy," a significant difference emerged. Using regression results from Table 3, Figure 1 [FIGURE:1] plots a curve with time as the horizontal coordinate and regression coefficient as the vertical coordinate, where vertical lines represent 5% confidence intervals. The effect size at time points within the [-5,-4] interval before policy implementation is close to 0, conforming to the parallel trend hypothesis, indicating no significant trend difference between treatment and control groups before policy implementation. From time point 1, the effect size continues to rise (0-5000), showing significant policy effects and sustainability.

4.2. Baseline Regression Results

In the previous section, a difference-in-differences (DID) model was established through implementation of the smart elderly care industry and passed the parallel trend test. Baseline regression results are presented in Table 3 [TABLE:3].

The data reflect the impact of the smart elderly care industry (Policy) on China's GDP under the influence of ODR, PPE, FE, and PTI control variables. According to the data, the explanatory variable (Policy) satisfies significance for the explained variable (GDP) at p < 0.01, with a coefficient of 1774. The control variables (ODR, PPE, FE, PTI) satisfy significance for GDP at the 95% confidence level, with coefficients of -2.286, 17.85, 4.659, and -149.1, respectively, and all control variables are significantly effective. This shows that implementation of the smart elderly care industry has a significant positive impact on GDP growth.

[TABLE:3]

5.1. Replace the Explained Variable

To address potential variable selection issues, this paper replaces government fiscal tax revenue (MOF) data with the original explanatory variable (GDP) for further analysis, obtaining regression results for each independent variable (see Table 4 [TABLE:4]).

According to the data, the impact of the smart elderly care industry (Policy) on government fiscal tax revenue (MOF) remains positively correlated, with a coefficient of 47.07. This exclusion of sporadic problems in explanatory variable selection shows that the benchmark regression is robust.

[TABLE:4]

5.2. Placebo Test

The main purpose of placebo testing is to serve causal inference, making research conclusions more reliable by eliminating interference from other possible factors. Its principle is testing whether the effect of real policy or treatment is reliable by constructing pseudo-policy or pseudo-treatment. In this study, to eliminate interference factors from green financial policies, this paper randomly selects samples, randomly generates policy implementation times, and constructs new smart elderly care industry experimental groups and policy implementation times.

As shown in the figure, after randomization, the coefficients of the smart elderly care industry policy effect (Policy) are normally distributed and concentrated around 0, with most p-values higher than 0.1. Figure 2 [FIGURE:2] shows the random coefficient located to the left of the coefficient 1774, indicating that after randomization, the policy effect is significantly weakened in both significance and effect strength, confirming that our main study is robust after excluding interference from other possible factors.

5.3. PSM-DID

To eliminate interference from sample self-selection problems, this paper adopts the propensity score matching (PSM) method. PSM is used to compare differences in outcome variables between experimental and control groups, finding samples with similar interference variable values through matching to reduce interference from data bias and confounding factors.

According to Table 5 [TABLE:5], before matching, the mean values of variables (PPE, FE, PTI, ODR) between experimental and control groups were significantly different, all reaching statistical significance (p < 0.05). Specifically, standardized deviations of PPE, FE, PTI, and ODR were 90.2%, 121.7%, 104.8%, and 16.3, respectively. These high standardized deviations indicated significant imbalance in key features between experimental and control groups before matching, potentially confounding treatment effect estimation. After matching, all deviations are significantly reduced, showing the effectiveness of the PSM method. The reduction in standardized deviation before and after matching is also substantial, reaching 14.6% (PPE), 54.5% (FE), 67.2% (PTI), and 39.1% (ODR), respectively. These reductions indicate that PSM effectively alleviates imbalance between experimental and control groups in key characteristics.

Therefore, through propensity score matching (PSM), the final PSM-DID result is obtained by eliminating interference from sample self-selection problems.

As shown in Table 6 [TABLE:6], before matching, the outcome variable (Y) coefficient of the experimental group was 37796.9, while that of the control group was 17954.32, showing a significant difference with ATT as high as 19842.58, corresponding to p < 0.01. This strongly indicates systematic differences between experimental and control groups before matching, possibly due to uneven covariate distribution. After matching, the Y mean of the outcome variable in the experimental group remained unchanged at 37796.9, while the control group mean increased to 23022.27. PSM reduces bias caused by covariate imbalance, making ATT estimates more robust. This helps define the magnitude of this effect more accurately and enhances credibility of causal inference. The positive effects of treatment on outcome variables were confirmed, and empirical rigor and internal validity of this conclusion were improved by the PSM method.

[TABLE:5]

[TABLE:6]

6.1. The Intermediary Role of Capital Investment in Smart Elderly Care Industry

From the capital investment perspective, smart pension policy first affects various smart pension industries, and industry development inevitably drives economic development. For example, the smart pension industry forms a compound model of "silver economy + digital economy" through technological innovation (Charness & Boot, 2017), becoming a new kinetic energy for economic transformation and upgrading while alleviating aging society pressures. Based on input-output theory (Leontief, 1936), smart elderly care industry development will also extend related industrial chains and form new economic growth points. To verify this theory, the following mediation effect model was established:

$$it = \alpha + \beta ME$$

$$it = \alpha + \beta ME it + \lambda C + \mu + \gamma t + \varepsilon_{it} \cdots GDP it + \lambda C + \mu + \gamma t + \varepsilon_{it} \cdots$$

$$GDP it = \alpha + \beta QT Y it = \alpha + \beta T AM it + \lambda C + \mu + \gamma t + \varepsilon_{iz} it + \lambda C + \mu + \gamma t + \varepsilon_{it}$$

Where QTY _{it} represents the number of registered smart elderly care companies in China, and TAM_{it} represents the market size of smart companies.

The mediation effect results of capital investment in smart pension industry are shown in Table 7 [TABLE:7]. According to the data, the coefficients of policy effect intensity (ME) of smart elderly care industry on registered number of smart elderly care enterprises (QTY) and market size (TAM) are 0.012 and 0.001, respectively, at a significant level of 1%. This shows that as China's population structure trends toward aging, effective demand also moves toward aging. Based on rapid development of technological levels and improvement of policy effect intensity, the smart elderly care industry has effectively promoted transformation of China's elderly care service industry toward smart elderly care. The pre-term coefficients of registered smart elderly care enterprises (QTY) and smart elderly care industry market size (TAM) on China's GDP are 30.315 and 2237.46, respectively, at a significant level of 1%, showing that smart elderly care industry development has a significant positive effect on China's economic development, driving China's economic growth. Therefore, it can be explained that smart elderly care policy indirectly promotes economic growth by promoting capital investment in the smart elderly care industry (M1). Hypothesis 2 (H2) is established.

[TABLE:7]

6.2. The Mediating Effect of Labor Efficiency

From the labor efficiency perspective, smart pension policy forces technological innovation and labor efficiency improvement by increasing labor costs and corporate operational pressures. For example, manufacturing and service industries have significantly improved production efficiency and reduced dependence on traditional labor by introducing intelligent robots and big data analytics. This technological innovation not only compensates for labor supply shortages but also promotes optimization and sustainable development of economic structure. Based on efficiency wage theory (Shapiro & Stiglitz, 1984), smart pension policies can promote economic growth. Similarly, to verify this theory, the following mediation effect model is established:

$$EP RB it = \alpha + \beta ME it + \lambda C + \mu + \gamma t + \varepsilon_{it} \cdots GDP it = \alpha + \beta EP RB it + \lambda C + \mu + \gamma t + \varepsilon_{it}$$

$$it = \alpha + \beta ME it + \lambda C + \mu + \gamma t + \varepsilon_{jt} \ldots GDP it = \alpha + \beta RD it + \lambda C + \mu + \gamma t + \varepsilon_{it}$$

Where EPRB _{it} represents labor productivity, and RD_{it} represents research expenditure.

The mediation effect results of labor efficiency are shown in Table 8 [TABLE:8]. According to the data, the coefficients of policy effect intensity (ME) on labor productivity (EPRB) and labor scientific research expenditure (RD) are 0.03 and 176.301, respectively, at a significant level of 1%. This shows that as policy effect intensity deepens, the forcing mechanism stimulates the driving force of technological innovation and efficiency improvement. The pre-term coefficients of labor productivity (EPRB) and labor scientific research expenditure (RD) on GDP are 13.502 and 0.001, respectively, at a significant level of 1%, showing that improvement in labor productivity (EPRB) and labor scientific research expenditure (RD) together constitute an important driving force for economic growth. Technological innovation input indirectly promotes economic efficiency through technological progress, while improvement in labor productivity directly optimizes resource allocation. The synergistic effect of the two significantly improves overall economic output, i.e., GDP. Hypothesis 3 (H3) is established.

[TABLE:8]

6.3. Mediating Effect of Consumption Structure

From the consumption structure perspective, smart elderly care policy has given birth to emerging elderly care services, which indirectly promotes economic growth by driving consumption structure upgrading. According to the extension of life cycle theory (Modigliani & Brumberg, 1954) in the aging context, the prolonged consumption cycle of elderly groups forms "third age consumption" (Gilleard & Higgs, 2005). Smart pension policy further provides supply, giving birth to the "silver economy." Similarly, to verify this theory, the following mediation effect model is established:

$$it = \alpha + \beta ME it = \alpha + \beta ME it + \lambda C + \mu + \gamma t + \varepsilon_{it} \cdots GDP it + \lambda C + \mu + \gamma t + \varepsilon_{it} \ldots$$

$$GDP it = \alpha + \beta CM C it = \alpha + \beta SSF it + \lambda C + \mu + \gamma t + \varepsilon_{it} it + \lambda C + \mu + \gamma t + \varepsilon_{it}$$

Where CMC _{it} represents per capita medical care expenditure of residents, and SSF_{it} represents basic pension insurance fund expenditure.

The mediation effect of consumption structure is shown in Table 9 [TABLE:9]. The data shows that the policy effect intensity (ME) of the smart pension industry has a significant effect at the 1% level on both per capita medical care expenditure (CMC) and basic pension insurance fund expenditure (SSF), with coefficients of 0.001 and 0.034, respectively. This shows that as policy effect intensity deepens, the policy indirectly promotes growth in consumption expenditure related to health and elderly care. The rise in health care spending (CMC) reflects increased demand for medical services by the elderly, while the increase in basic pension insurance fund spending (SSF) reflects demand for funds from the social security system in an aging society. This shows that smart pension policy has not only changed residents' consumption structure but also significantly increased social expenditure on pension and medical security. The pre-term coefficients of basic pension insurance fund expenditure (SSF) and per capita medical care expenditure (CMC) are 0.357 and 266.782, respectively, at a significant level of 1%, showing that as the policy effect process advances, consumption expenditure related to the elderly has a significant positive effect on economic growth. This not only reflects the rise of the "silver-haired economy" market but also means that growing demand from middle-aged and elderly people in an aging society is becoming an important driving force for economic development.

Smart pension policy has given birth to the "silver-haired economy" (M3) and driven consumption structure upgrading, thus injecting new vitality into GDP growth. Hypothesis IV (H4) holds.

[TABLE:9]

7. Heterogeneity Analysis

To further explore and verify the heterogeneity of smart elderly care industry policies across different regions on economic development, this paper adopts Hansen's (1999) threshold regression model, setting threshold variables across different dimensions to explore heterogeneity at various levels in each region. The model is established as follows:

$$Y_{it} = \mu_{i} + \beta X_{it} \cdot I q_{it} \leq \theta X_{it} \cdot I q_{it} > \theta + \gamma Z_{it} + \epsilon_{it}$$

Where Y _{it} is the explained variable (observed value of individual i at time t), X_{it} is the explanatory variable, q _{it} is the threshold variable, is the threshold value to be estimated, Z_{it} is the control variable, and I is the indicator function (taking 1 when the condition is met, 0 otherwise).

According to the chart, setting GDP as the threshold variable, due to different economic development statuses of each provincial administrative unit, the impact of smart pension policy effect intensity (ME) on economic growth shows significant heterogeneity. According to the test in the LR figure on the left, this heterogeneity has a significant double threshold effect. Further analysis shows that the first threshold value of the threshold variable (GDP) is 18457.27, and the second threshold value is 41781.29. Therefore, we can set the threshold variable to three intervals: GDP less than 1,845.727 billion yuan, between 1,845.727 billion and 4,178.129 billion yuan, and GDP greater than 4,178.129 billion yuan.

Through threshold effect analysis (see Figure 3 [FIGURE:3]), when GDP is less than 1,845.727 billion yuan (represented by 0 in Table 10 [TABLE:10]), the impact coefficient of smart pension policy effect intensity (ME) on economic growth is -0.1082424, indicating that smart pension policy has no significant impact on economic growth. When GDP is between 1,845.727 billion and 4,178.129 billion yuan (represented by 1 in Table 10), the pre-term coefficient is 0.0907052, indicating that policy effects gradually emerge. When GDP is greater than 4,178.129 billion yuan (represented by 2 in Table 10), the pre-term coefficient is 0.256091, and the policy significantly promotes economic growth. The data proves that cities with faster economic development and higher total volume (such as eastern coastal and southern regions of China) are more affected by smart elderly care policy, while cities with slower economic development and lower total volume (such as western regions) are less affected. Therefore, to promote more "silver-haired economy" development through the smart elderly care industry, priority should be given to urban areas with rapid economic development and high total volume.

7.1. Analysis of Heterogeneity of Economic Development

According to the above model, GDP is set as the threshold variable to study the impact of smart pension policy effects on economic growth in regions with different economic development levels. The results obtained from data analysis are shown in Figure 3 [FIGURE:3] below.

7.2. Heterogeneity Analysis of Aging Degree

Consistent with the previous section, the population aging rate (ODR) is set as the threshold variable to study the impact of smart pension policy effects on economic growth in areas with different aging degrees. The results obtained from data analysis are shown in Figure 4 [FIGURE:4] below.

According to the chart, setting population aging rate (ODR) as the threshold variable, due to different population structures across provinces (i.e., different population aging rates), the impact of smart pension policy effect intensity (ME) on economic growth shows significant heterogeneity. According to the test in the LR figure on the left, this heterogeneity has a significant single threshold effect. Further analysis shows that the threshold value of the threshold variable (ODR) is 12.31. Therefore, we can set the threshold variable to two intervals: population aging rate less than 12.31% and population aging rate greater than 12.31%.

Through threshold effect analysis (see Figure 4 [FIGURE:4]), when the population aging rate is less than 12.31% (represented by 0 in Table 11 [TABLE:11]), the impact coefficient of smart pension policy effect intensity (ME) on economic growth is 0.0815708, indicating that smart pension policy has little impact on economic growth. When the population aging rate is greater than 12.31% (represented by 1 in Table 11), the impact coefficient is 0.4067166, and the policy significantly promotes economic growth.

Therefore, through analysis, regions with higher aging degrees (such as Northeast China, North China, and Central China) are more affected by smart pension policies, while regions with lower aging degrees (such as western China) are less affected. To promote more "silver-haired economy" development through the smart elderly care industry, areas with higher aging populations should be prioritized.

[TABLE:10]

[TABLE:11]

8. Discussion

The findings reveal that smart pension policies significantly promote economic growth through capital investment, labor efficiency, and consumption structure upgrades, with notable regional heterogeneity. These results align with prior studies emphasizing the role of policy-driven technological innovation in aging societies (Zhao et al., 2021; Chen et al., 2021). However, our analysis diverges from Han et al. (2019), who argued that less-developed regions benefit more from digital economy spillovers. This discrepancy may stem from differing focal points: while Han et al. prioritized innovation efficiency and regional balance, our study highlights the foundational role of advanced digital infrastructure and industrial ecosystems in economically developed regions (e.g., eastern China). For instance, superior hardware support and intelligent computing power in regions like Beijing and Guangdong amplify the effectiveness of AI-driven smart pension innovations, whereas central and western regions, constrained by weaker technological infrastructure and human capital gaps, rely more on policy spillovers and imitation-based innovation.

Furthermore, the threshold model underscores that regions with higher GDP and aging rates exhibit stronger policy effects, corroborating the "comparative advantage" logic observed in Cao's (2018) analysis of manufacturing platforms. Similar to the dual roles of "Consumer Internet Platform" and "Industrial Internet" in Cao's framework, smart pension policies in eastern China leverage demand-side traction (e.g., high-end elderly consumption) and supply-side optimization (e.g., industrial chain integration), whereas central and western regions face structural bottlenecks in translating policy into actionable outcomes. This regional asymmetry suggests that policymakers must adopt gradient strategies: prioritizing technological empowerment in developed regions while enhancing basic digital accessibility and human capital investment in underdeveloped areas. Such an approach would align with the "three-dimensional system" proposed in this study, balancing demand identification, technology empowerment, and policy coordination to maximize the silver economy's potential across diverse contexts.

9. Conclusions and Recommendations

By constructing the DID model, this paper uses the smart pension policy as an external shock experiment to explore the impact of policy effect (Policy) on GDP. Regarding influence mechanisms, this paper examines the mediating effects of capital input, labor supply, and consumption structure. This paper further explores heterogeneity of effects across different regional economic development levels and population aging structures. This study enriches research on the impact of smart elderly care industry policies on GDP. Compared with previous literature, our study explores for the first time the impact of smart elderly care policies on GDP at the provincial level using provincial administrative unit panel data from 2008 to 2023, and examines its characteristic mechanisms. It also confirms Zhao Qiang et al.'s (2021) finding that regional differences in aging lead to different policy effects, as well as heterogeneity problems caused by economic differences. The main conclusions are as follows.

First, smart pension policy has a significant positive impact on economic growth. Second, it confirms the mediating effect of smart elderly care policy through three perspectives: capital investment, labor supply, and consumption structure. Smart elderly care policy promotes transformation of China's elderly care service industry toward smart elderly care; the forcing mechanism stimulates the driving force of technological innovation and efficiency improvement; and drives consumption structure upgrading to give birth to "silver-haired economy" development, promoting China's economic growth. Finally, the promotion effect of smart pension policy is heterogeneous, with regional economic development level and regional aging degree having heterogeneous effects on smart pension policy. The policy has more significant effects on provinces with faster economic development and higher total volume (such as eastern coastal and southern regions) and regions with higher aging degrees.

Based on these findings, this paper puts forward several policy recommendations:

First, continue to promote the development experience of smart elderly care policies, establish a more effective evaluation system for smart elderly care projects, make up for shortcomings of traditional elderly care service policies, support R&D of smart elderly care enterprises, and further promote enterprise intelligent transformation. It is crucial to focus on both quantity and quality of the smart elderly care industry and shift from performance appraisal-oriented to actual environmental quality improvement.

Second, increase the role of policies in promoting transformation of the smart elderly care industry, expand the scale of the smart elderly care industry, and improve efficiency and quality of elderly care services, thereby reducing family and social elderly care burdens, reducing care costs, and releasing more labor resources.

Third, increase the "forcing mechanism" of policies on technological innovation and development, accelerate application of automation, artificial intelligence, and digital technologies to compensate for labor supply shortages. Further promote optimization of economic structure and sustainable policy development, implement structural reforms in the labor market, alleviate social pressure from aging, improve medical service efficiency, and reduce medical costs.

Fourth, promote policy reform at the consumption structure level, improve innovation of smart elderly care industry in medical and health care, elderly care services, leisure tourism, intelligent equipment, and other fields to better align with consumption needs of the elderly population, give birth to the "silver-haired economy," and promote consumption structure upgrading and formation of new growth points.

Fifth, for areas with weak national economic development levels, inspection and incentives should be strengthened during smart elderly care implementation. For example, in government performance evaluation, the proportion of indicators for high-tech industries and elderly care service industries should be increased to accelerate promotion of the smart elderly care industry. In areas where smart elderly care policies are weak, the government should pay more attention to promoting synergy among smart elderly care enterprises to stimulate significant positive impacts on economic growth.

Finally, for areas with low national aging degrees, policy orientation can be appropriately improved, emphasizing forward-looking layout and preventive policy design, combining regional characteristics to promote integrated development of smart elderly care with local economy and technology. Preventing aging is the soft power of the country.

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