The Impact of Aging and Urbanization on CO2 Emission in Chinese Cities: An Empirical Analysis

The Impact of Aging and Urbanization on CO2 Emission in Chinese Cities: An Empirical Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Impact of Aging and Urbanization on CO 2 Emission in Chinese Cities: An Empirical Analysis Yongchun Zhao, Mengzhen Zhao, Chi Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6852089/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in npj Urban Sustainability → Version 1 posted 9 You are reading this latest preprint version Abstract Rapid aging and urbanization pose major challenges to global CO 2 reduction efforts, particularly in China. As such, effective carbon reduction strategies must account for their combined impact on emissions. However, existing research pays insufficient attention to the combined impact of aging and urbanization on CO 2 emissions and the underlying economic drivers. Furthermore, city-level analyses for forecasting emissions related to these demographic shifts remain scarce. To address these limitations, this study constructs a STIRPAT model using city-level data from China in 2010–2020 to estimate the combined effects of population aging and urbanization on CO 2 emissions. Then, we explore the economic drivers underlying this relationship. Finally, we project city-level CO 2 emissions driven by population aging and urbanization under different shared socioeconomic pathways (SSPs) in 2030–2060. Our findings suggest that: (a) The emission-increasing effect of aging outweighed the mitigating impact of urbanization, and together these opposing forces contributed to the overall rise in emissions. (b) Per capita disposable income and the size of the tertiary sector are key economic drivers of population aging, contributing to increased CO 2 emissions. The tertiary sector also significantly influences urbanization, which in turn facilitates emission reductions. (c) The relative contribution of CO 2 emissions from population aging and urbanization is similar under SSP1, SSP4, and SSP5. However, the absolute levels of CO 2 emissions resulting from the combined effects of aging and urbanization exhibit significant variation across cities under all SSP1-SSP5 scenarios. Base on the results, we offer policy recommendations to support carbon mitigation efforts. Earth and environmental sciences/Environmental social sciences Social science/Anthropology Social science/Economics Social science/Environmental studies Social science/Social policy Figures Figure 1 Figure 2 Introduction Rising global aging and urbanization pose significant challenges to carbon reductions and achieve Paris Agreement targets. According to the World Population Prospects 2022 1 , the global population aged 65 and above is increasing faster than that under 65, and projecting to rise from 10% in 2022 to 16% by 2050. Meanwhile, the World Urbanization Prospects 2018 2 reports that 55% of the global population are urban dwellers, and projects to reach 68% by 2050, with most growth concentrated in Asia and Africa. China is one of the fastest-aging and urbanizing countries in the world over the past and coming decade 3 , 4 , with these shifts posing significant challenges to achieve carbon neutrality by 2060. It is crucial for China to clarify the combined effects of population aging and urbanization on CO 2 emissions and to formulate targeted mitigation strategies in response. In reality, urbanization reshapes the age structure of urban and rural populations, while aging influences the pace and quality of future urbanization 5 . These interactions highlight the importance of integrating aging and urbanization within a unified analytical framework to assess their combined effects on CO 2 emissions. However, existing studies generally examined the directly impact of population aging or urbanization on CO 2 emissions in isolation. An aging society increased demand for healthcare and assisted living industries 6 while also driving up energy consumption for heating, cooking, and public transportation 7 . Urban residents rely less on biomass fuels for heating and cooking, resulting in lower household CO 2 emissions compared to rural areas 8 . Particularly in regions with high production efficiency, well-developed information networks, and advanced technological adaptation, urbanization leveraged economies of scale to promote population and industrial agglomeration 4 , thereby directly improving energy efficiency and reducing CO 2 emissions. While the impacts of population aging and urbanization on CO 2 emission are largely driven by regional economic conditions 9 , limited research has explored the specific economic factors that influence these demographic trends and, in turn, affect CO 2 emissions. Existing studies on economic-driven factors have shown that lower-income elderly populations tended to adopt less on environmentally friendly consumption habits, resulting in higher CO 2 emissions 8 . Conversely, wealthier households, having greater financial resources and choices, prefer to use cleaner energy sources like electricity and natural gas 10 . Additionally, Chikaraishi et al. (2015) 11 found that higher per capita GDP and a greater service-sector share contribute to more environmentally sustainable urbanization. Collectively, these findings highlight the academic importance of exploring the economic drivers of demographic changes and the resulting impacts of those changes on CO 2 emissions, as closing this research gap is essential for informing effective, economically grounded carbon reduction policies. Furthermore, while existing studies have predicted future CO 2 emissions trends related to population aging and urbanization at the national 12 or provincial level 13 , they often overlook city-level forecasts, limiting the ability to reveal regional differences in China’s future emissions. This study empirically examines the combined effects of population aging and urbanization on CO 2 emissions by (a) constructing a city-level STIRPAT model that accounts for the endogeneity of aging and urbanization; (b) applying the Control Function Approach (CFA) to analyze correlations among economic factors; and (c) projecting city-level CO 2 emissions in China from 2030 to 2060 under the five SSP scenarios (SSP1-SSP5) defined in the IPCC Sixth Assessment Report 14 . Results CO 2 emissions impacts of aging and urbanization Using city-level data from 2010 to 2020 in China, we conduct an empirical analysis of the combined effects of population aging and urbanization on CO 2 emissions. The result shows that (Table 1), China’s CO 2 emissions exhibited an upward trend, with aging and urbanization exert opposing effects. Specifically, the results obtained from the instrumental variable (IV) approach indicate a positive relationship between population aging ( PAG ) and CO 2 emissions, and a negative relationship between urbanization ( URB ) and CO 2 emissions. Table 1 reports the estimates under both exogeneity and endogeneity assumptions, using fixed effects, random effects, 2SLS, 2SPS and 2SRI models. Based on the test outcomes, we select the 2SRI model for detailed regression analysis. Supplementary Table 4 presents the detail results of tests for multicollinearity, endogeneity, and the validity of the instrumental variables. The 2SRI regression results show that (Table 1), PAG is significantly positively associated with CO 2 emissions at the 1% significance level, with a one-unit increase in aging is expected to raise CO 2 emissions by 3.007 units (e 1.101 =3.016). URB is significantly negatively associated with CO 2 emissions at the 5% significance level, with a one-unit increase in urbanization expected to reduce emissions by 0.409 units (e − 0.893 =0.409). Column 5 shows that the first-stage residuals are statistically significant in CO 2 emissions at the 1% and 10% levels, indicating that the validity of the instrumental variables and the appropriateness of the 2SRI estimation method. The finding that population aging is positively associated with CO 2 emissions is consistent with Yu et al. (2018) 7 . Similarly, the negative association between urbanization and CO 2 emissions is consistent with Ala-Mantila et al. (2014) 15 , which argues that, given equal levels of consumption and expenditure, urban lifestyles result in less environmental damage than rural ones. Table 1 Comparative regression results of aging and urbanization on CO 2 emissions Variables Exogenous hypothesis Endogenous hypothesis (1) RE (2) FE (3) 2SLS (4) 2SPS (5) 2SRI PAG -0.126*** (-4.116) [0.000] -0.085*** (-2.699) [0.007] 1.057*** (3.231) [0.001] 1.062*** (3.184) [0.001] 1.101*** (3.173) [0.002] URB -0.019 (-0.446) [0.655] -0.053 (-1.195) [0.232] 0.007 (0.018) [0.986] -0.676 (-1.527) [0.127] -0.893** (-2.038) [0.042] PGDP 0.275*** (17.960) [0.000] 0.270*** (17.389) [0.000] 0.239*** (2.869) [0.004] 0.326*** (3.718) [0.000] 0.346*** (3.845) [0.000] EDU 1.006*** (5.145) [0.000] 0.654*** (3.202) [0.001] 3.991*** (3.776) [0.000] 5.589*** (4.829) [0.000] 6.139*** (5.366) [0.000] STR 0.144*** (7.013) [0.000] 0.139*** (6.594) [0.000] 0.229*** (7.856) [0.000] 0.233*** (8.212) [0.000] 0.233*** (7.860) [0.000] INS 0.339*** (21.192) [0.000] 0.324*** (19.235) [0.000] 0.429*** (20.191) [0.000] 0.409*** (18.014) [0.000] 0.402*** (18.021) [0.000] Constants -0.472 (-1.091) [0.275] 0.403 (0.887) [0.375] -10.453*** (-6.311) [0.000] -11.420*** (-6.806) [0.000] -11.892*** (-7.000) [0.000] Residuals from PAG -1.437*** (-4.063) [0.000] Residuals from URB 0.817* (1.796) [0.072] obs 2200 2200 2200 2200 2200 No. of cities 200 200 200 200 200 Notes : The values in parentheses in Columns 1 and 2 represent t-values, while those in Columns 3 to 5 correspond to z-values. The values in square brackets represent p-values. Significance levels (2-tailed) are denoted as follows: * p < 0.1, ** p < 0.05, *** p < 0.01. Table legend Columns 1 and 2 report the results assuming that all variables are exogenous, while columns 3–5 display the coefficient estimates obtained using 2SLS, 2SPS, and 2SRI, respectively, under the assumption that both PAG and URB are endogenous variables. Unlike the results in columns 1 and 2, the consistent regression results showed in columns 3–5 indicate a positive relationship between PAG and CO 2 emissions. Additionally, the results in columns 4 and 5 show a negative relationship between URB and CO 2 emissions. The influence of economic factors on aging, urbanization, and CO 2 emissions The existing literatures suggested that regional economic development closely drives population aging and urbanization 16 . We further employ the CFA method to examine how economic factors drive the effects of aging and urbanization on CO 2 emissions in China. As illustrated in Table 2a, columns 1–4 present the first-stage regression results, showing that fiscal revenue ( REV ), per capita disposable income ( PINC ), and the share of the tertiary sector ( TER ) are all significantly associated with PAG . Specifically, REV is significantly negatively correlated with PAG at the 1% significance level, while PINC is significantly negatively correlated with PAG at the 5% significance level. A possible explanation is that higher fiscal revenue and Per capita disposable income enhance a city’s attractiveness to migrant workers, leading to an inflow of labor that reduces the proportion of the elderly population. In contrast, TER is significantly positively correlated with PAG at the 1% significance level. According to the Petty-Clark theorem 17 , as economic development progresses and per capita national income increases, the service sector gradually becomes the primary driver of economic growth. The empirical results suggest that during economic growth and industrial restructuring, if the total urban population remains constant, population aging will increase alongside economic expansion. Column 6 shows that PAG driven by PINC is significantly positively correlated with CO 2 emissions at the 1% significance level. Column 7 indicates that PAG driven by TER is also significantly positively correlated with CO 2 emissions at the 1% significance level. As illustrated in Table 2b, columns 1 and 2 indicate that neither REV nor PINC is significantly correlated with URB . Columns 3–4 show that TER and fixed asset investment ( INV ) are both significantly negatively correlated with URB at the 1% significance level. Column 5 indicates that URB associated with TER is significantly negatively correlated with CO 2 emissions at the 5% significance level. Based on these findings, this study concludes that per capita disposable income and the size of the tertiary sector are key drivers of population aging, which, in turn, is significantly positively correlated with CO 2 emissions. Additionally, the size of the tertiary sector is an important driver of urbanization, which, in turn, is significantly negatively associated with CO 2 emissions. Table 2 Estimation results for economic factors using the CFA method a. Estimation results of population aging effects (1) PAG (2) PAG (3) PAG (4) PAG (5) CO 2 (6) CO 2 (7) CO 2 Income Factors REV -0.077*** (-8.353) [0.000] PINC -0.128** (-2.387) [0.017] Expenditure Factors TER 0.088*** (3.962) [0.000] INV -0.007 (-0.666) [0.506] Revenue-driven population aging -0.057 (-0.410) [0.682] Per capita disposable income-driven population aging 2.003*** (15.264) [0.000] Tertiary-driven population aging 2.198*** (18.569) [0.000] Residuals from column (1) 0.023 (0.147) [0.883] Residuals from column (2) -2.282*** (-16.257) [0.000] Residuals from column (3) -2.503*** (-19.918) [0.000] Controls YES YES YES YES YES YES YES Observations 2200 2200 2200 2200 2200 2200 2200 b. Estimation results of urbanization effects (1) URB (2) URB (3) URB (4) URB (5) CO 2 (6) CO 2 Income Factors REV 0.002 (0.250) [0.803] PINC 0.033 (0.842) [0.400] Expenditure Factors TER -0.136*** (-6.138) [0.000] INV -0.054*** (-6.596) [0.000] Tertiary-driven urbanization -0.872** (-1.978) [0.048] Investment-driven urbanization -3.091*** (-30.617) [0.000] Residuals from column (3) 0.828* (1.814) [0.070] Residuals from column (4) 3.168*** (30.781) [0.000] Controls YES YES YES YES YES YES Observations 2200 2200 2200 2200 2200 2200 No. of cities 200 200 200 200 200 200 Notes : The values in parentheses in Columns 1–4 represent t-values. In Columns 5–7 of Table 5a and Columns 5–6 of Table 5b, the values in parentheses represent z-values. The values in square brackets represent p-values. Significance levels (2-tailed) are denoted as follows: * p < 0.1, ** p < 0.05, *** p < 0.01. Table legend The two tables separately present the relationship between economic factors ( REV , PINC , TER , INV ) and population aging, as well as between the same economic factors and urbanization. And then, we further illustrate how aging and urbanization, when significantly influenced by economic factors, relate to CO 2 emissions. This study controls for the remaining variables in the STIRPAT model, and since these control variables are not the focus of the analysis, their estimated coefficients are not presented in detail. The regression coefficients of the control variables are available from the authors upon request. Aging and urbanization-related CO 2 emissions from 2030 to 2060 The projected rapid growth of population aging and urbanization under all scenarios 18 is expected to pose significant challenges for China in achieving its carbon neutrality goal. Based on the coefficient estimation results from the empirical model, this study projects China’s future CO 2 emissions for 2030–2060 at city level under different SSP scenarios. With a mean absolute percentage error (MAPE) of 19%, which falls within the 10–20% range defined by Pao and Tsai (2011) 19 as indicative of good forecast accuracy, the use of STIRPAT regression coefficients to predict CO 2 emissions for 2030–2060 is feasible. The combined effects of population aging and urbanization contribute to the overall growth of CO 2 emissions, with the positive impact of population aging on emissions in all projected scenarios outweighs the negative impact of urbanization. Among 200 Chinese cities, SSP3 records the lowest CO 2 emissions influenced by the combined effects of population aging and urbanization, with 4663.29 Mt in 2030. In contrast, SSP5 shows the highest emissions influenced by the combined effects, reaching 5807.18 Mt in 2030. Supplementary Table S5 reports the projected total CO 2 emissions in detail. Among all scenarios, Fig. 1 shows that SSP3 also has the lowest share of emissions influenced by the combined effects relative to total projected emissions, at 30.86% in 2030 and 48.32% in 2060, followed by SSP2, SSP4, and SSP5, while SSP1 has the highest proportion. Under SSP1, SSP4, and SSP5, the proportion of CO₂ emissions attributable to aging and urbanization is relatively similar, reaching 30.02%, 30.22%, and 29.94% respectively in 2030, and increasing significantly to 46.19%, 47.36%, and 45.43% by 2060. SSP1 exhibits the highest degree of population aging among all SSP scenarios 18 , with the positive impact of population aging on CO 2 emissions reaching 39.72% in 2030 and rising to 70.29% by 2060, indicating the greatest pressure for carbon emission growth. Conversely, the negative impact of urbanization is most pronounced under SSP5, accounting for 9.73% in 2030 and 24.46% in 2060, slightly higher than those under SSP1 and SSP4. Its highest level of urbanization among all scenarios 18 reflects the greatest potential for emission reduction. City-level CO 2 emissions disparities in 2060 Although the relative contribution of CO 2 emissions associated with population aging and urbanization in total CO 2 emissions shows similar across SSP1, SSP4, and SSP5, this study further examines the absolute differences under various scenarios. Our findings suggest that the combined effects of aging and urbanization will lead to substantial variation in future CO 2 across cities, with marked regional disparities. Overall (Fig. 2), in 2060, CO 2 emissions associated with these demographic shifts are projected to be lower in cities along the southeastern coast and higher in cities located in the northwest and northeast regions. Under the SSP2 scenario, 35 cities, including Taiyuan, Zibo, and Weifang, fall into the lowest range of CO 2 emissions, where future CO 2 emissions are expected to be more influenced by urbanization than by population aging, suggesting a greater potential for achieving carbon reduction targets compared to other cities. Conversely, CO 2 emissions in the highest range under the SSP2 scenario include Changzhou, Yan’an, Shenyang, among a total of 40 cities. In these cities, future CO 2 emissions are expected to be more influenced by population aging rather than urbanization, leading to greater carbon emission pressures compared to other cities. Compared to SSP2 scenario, some cities under the SSP1 scenario, such as Hengyang, Yingtan, and Linyi, are expected to transition from lower to higher emission levels, among a total of 39 cities. These cities are primarily located in central and northern China, suggesting that the green development pathway of SSP1 has improved living standards and life expectancy, which in turn has accelerated population aging and increased CO 2 emissions. Moreover, the projected CO 2 emissions under SSP1 in Lianyungang, Yancheng, Zhenjiang, and Yangzhou have decreased compared to those under SSP2, with these cities shifting from higher emission levels under SSP2 to lower ones under SSP1. This indicates that SSP1 has enhanced urban attractiveness, facilitated rural-to-urban migration, accelerated urbanization, and consequently reduced projected CO 2 emissions. Under the SSP3 scenario, the overall level of urbanization is higher compared to SSP2, leading to lower projected CO 2 emissions. Notably, no cities experience a transition from lower to higher emission levels under SSP3 relative to SSP2. A total of 137 cities, including Jian, Suqian, and Shaoyang, are projected to shift from higher CO 2 emissions levels under SSP2 to lower levels under SSP3. These cities are primarily located in eastern and central China, indicating that despite intensified regional competition and resource constraints under SSP3, they remain relatively attractive to populations, which in turn drives deeper urbanization and contributes to lower CO 2 emissions. In most cities, projected CO 2 emissions under SSP4 are higher than under SSP2. Fifty cities, including Suzhou, Xiangxi, and Suizhou, are expected to shift from lower emission levels under SSP2 to higher levels under SSP4. In these cities, population aging is larger compared to SSP2. Due to the regional disparities under SSP4, projected CO 2 emissions in many cities are lower than under SSP2. Fourteen cities such as Beihai, Bozhou, and Laibin are expected to shift from higher emission categories in SSP2 to lower ones in SSP4. This indicates that these cities may experience higher levels of urbanization than under SSP2, contributing to future reductions in CO 2 emissions associated with both population aging and urbanization. The total projected CO 2 emissions under SSP5 are higher than those under SSP2. This indicates that in the fossil fuel-driven, high-growth pathway of SSP5, the emission increases associated with population aging outweigh the reductions from urbanization. Seventy-five cities, including Huaihua, Xiangxi, and Qiandongnan, are projected to have higher CO₂ emissions under SSP5 than under SSP2, with a corresponding shift to higher emission categories. Meanwhile, a few cities show lower emissions under SSP5 compared to SSP2. These cities have experienced a significant influx of rural migrants, resulting in rapid urban population growth and a lower share of elderly residents. However, the resulting changes in CO 2 emissions are not substantial enough to shift their emission categories. Discussion This paper integrates population aging and urbanization into a unified model, setting it apart from previous studies that analyze them separately or rely only on elderly samples to assess the impact of urbanization. We account for potential endogeneity between these two demographic shifts factors and CO 2 emissions, to obtain consistent estimates of the combined effects of aging and urbanization. The estimation results indicate that the positive effect of population aging on CO 2 emissions exceeds the negative effect of urbanization, suggesting that the accelerating aging process is reshaping emissions structure and diminishing the carbon reduction potential traditionally attributed to urbanization. There is a significant positive correlation between population aging and CO 2 emissions in Chinese cities, and this relationship is primarily driven by per capita disposable income and the share of the tertiary sector. As physical function declines with age, the elderly require higher indoor temperatures to ensure a healthy and comfortable living environment 20 . In China, coal remains the dominant fuel for household heating, and its use rises with the growing elderly population, contributing to higher carbon emissions associated with population aging. Moreover, nostalgia and conservative consumption habits reduce elderly’s motivation to purchase energy-efficient products 21 . If the elderly population lives in older buildings with outdated infrastructure, it can further lead to inefficient use of energy for heating, cooling, thereby contributing to higher CO 2 emissions. Rising urban incomes attract more migrant labor, leading to population growth and a decline in the aging rate. However, rising income levels do not directly alter the consumption patterns of the elderly. Instead, the combined effect of higher incomes and greater purchasing power further intensifies the CO 2 emissions associated with population aging. On the other hand, the growth of tertiary industries such as technology and information services is able to support medical innovation and improve elderly care. This not only extends life expectancy and accelerates the aging trend but also stimulates new consumption patterns among the elderly, further contributing to rising CO 2 emissions pressures. As aging and urbanization increasingly interact in China, the effectiveness of traditional emission reduction strategies that focus solely on urbanization diminishes. Consequently, policymakers must integrate demographic factors into policy design and explore alternative approaches to emission reduction. This study suggests that the government promote low-carbon awareness among the elderly and encourage the adoption of energy-efficient products and services. Specific measures include increasing subsidies for low-carbon household appliances and expanding the range of eligible subsidized products, while also strengthening efforts to promote these low-carbon consumption activities among the elderly population. Meanwhile, the government and enterprises should enhance urban appeal to highly skilled labor by reasonably adjusting wage structures and steadily increasing income levels. China’s urbanization exhibited a significant negative correlation with CO 2 emissions, and this relationship is primarily driven by the share of the tertiary sector. We argue that urbanization affects emissions through both production and consumption behavior. On one hand, urbanization is accompanied by advancements in human capital and technology, which enhance energy efficiency and help to reduce CO 2 emissions 22 . On the other hand, given urban residents’ strong willingness to adopt low-carbon products and their widespread use of green appliances powered by renewable energy, further urbanization can effectively promote green consumption, thereby helping to reduce CO 2 emissions 23 . The growth of the tertiary sector generates new urban employment opportunities 24 and typically involves lower energy consumption intensity. However, the shift toward industries distinct from labor-intensive or traditional agricultural sectors poses challenges for rural migrants entering cities, particularly regarding the skills and learning capacity required. This mismatch may lead to labor supply instability in the tertiary sector and, consequently, limit urban attractiveness to the rural population in China. Therefore, this study does not reveal a statistically significant positive correlation between the proportion of the tertiary sector and urbanization. Based on these findings, governments should offer skills training to support rural laborers meet the demands of emerging job opportunities, thereby improving the alignment between labor supply and market demand. At the same time, policy guidance on energy conservation for certain energy-intensive industries within the tertiary sector should be strengthened, with increased investment in energy-saving technologies and strict penalties imposed for illegal energy use. From 2030 to 2060, the proportion of CO 2 emissions attributable to population aging in Chinese cities is projected to exceed that linked to urbanization. The positive effect of population aging on emissions and the negative effect of urbanization are both expected to intensify over time. The relative contribution of CO 2 emissions associated with population aging and urbanization in total CO 2 emissions remains similar across SSP1, SSP4, and SSP5. However, the absolute impact of the combined effects of aging and urbanization is projected to cause substantial variation in future CO 2 across cities. By 2060, cities with lower predicted CO 2 emissions related to population aging and urbanization will be primarily concentrated in central and southeastern China, while those with medium to high emissions will be mainly located in the northern and northeastern regions. The future trajectory of CO 2 emissions associated with population aging and urbanization will be shaped by the characteristics of projected scenarios, as well as by existing demographic and urban development patterns across cities. SSP1 and SSP5 exhibit significant aging trends, resulting in higher projected CO 2 emissions compared to SSP2. In contrast, SSP3 features accelerated urbanization, leading to lower emissions than SSP2. SSP4 presents a more complex picture, as some cities are projected to exceed SSP2 in emissions while others may fall below, reflecting a highly uneven distribution. The study has certain limitations that should be noted. CO 2 emissions projections for 2030–2060 related to population aging and urbanization are based on regression coefficients derived from data spanning 2010 to 2020. This method does not account for cohort differences in savings and consumption preferences 8 . In particular, earlier-born elderly populations generally display lower consumption and higher savings tendencies, which are reflected in lower historical regression coefficients. Consequently, applying these historical coefficients may underestimate future CO 2 emissions associated with aging. Future research can further identify and analyze the evolving behavioral patterns of different elderly cohorts to improve the accuracy of long-term emissions projections. Methods Basic model foundation This study employs the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, originally developed by Dietz and Rosa (1997) 25 based on the IPAT Eq. 2 6 (Eq. 1), to empirically examine the correlation between aging, urbanization and CO 2 emissions. Compared to other models, the STIRPAT model is more suitable for examining the intrinsic relationship between human activities and environmental protection, as it offers greater flexibility to expand the model by adding, removing, or decomposing variables, thereby enhancing its analytical and explanatory capacity 27 . To ensure data stationarity and facilitate computation, we transform the basic STIRPAT model into a logarithmic form 28 (Eq. 2). Urbanization is then incorporated into the basic STIRPAT model (Eq. 3) to assess the combined impact with aging on CO 2 emissions. \(\:\text{I}\text{=}\text{a}{\text{P}}^{\text{b}}{\text{A}}^{\text{c}}{\text{T}}^{\text{d}}\text{e}\) (1) \(\:\text{ln}\text{I}\text{=}\text{ln}\text{a}\text{+}\text{b}\text{ln}\text{P}\text{+}\text{c}\text{ln}\text{A}\text{+}\text{d}\text{ln}\text{T}\text{+}\text{ln}\text{e}\) (2) \(\:\text{ln}{\text{CO}}_{\text{2}}\text{=}\text{ln}\text{a}\text{+}{\text{b}}_{\text{1}}\text{ln}\text{PAG}\text{+}{\text{b}}_{\text{2}}\text{ln}\text{URB}\text{+}{\text{b}}_{\text{3}}\text{ln}\text{PGDP}\text{+}{\text{b}}_{\text{4}}\text{ln}\text{EDU}\text{+}{\text{b}}_{\text{5}}\text{ln}\text{STR}\text{+}{\text{b}}_{\text{6}}\text{ln}\text{INS}\text{+}\text{ln}\text{e}\) (3) Where, I , P , A and T represent the environment factors, population, affluence, and technological progress, respectively. CO 2 , PAG , URB , PGDP , EDU , STR , and INS represent CO 2 emissions, population aging, urbanization, per capita GDP, average years of education per capita, energy structure, and energy intensity, respectively. a , b , c , and d are parameters to be estimated, and e represents the random disturbance term. Assessment of coefficient To obtain consistent, unbiased, and efficient estimation, this study employs the Instrumental Variables (IV) method to address endogeneity issues. A valid instrumental variable must satisfy three conditions: (a) it must be strongly correlated with the endogenous variables, (b) it must be exogenous to the model, meaning uncorrelated with the random disturbance term, and (c) the number of instrumental variables must be at least equal to the number of endogenous variables. This study selects the gender ratio ( GEN ) as the instrumental variable for PAG . GEN , defined as the number of males per 100 females, increases with a rise in the male population. It influences population size by affecting marriage and fertility rates, as well as migration driven by employment opportunities or marital considerations 29 . Moreover, GEN has no significant direct impact on CO 2 emissions, reinforcing its suitability as an instrument. This study uses the share of tertiary sector in GDP ( TER ) as the instrumental variable for URB . Growth in the tertiary industry stimulates urban economic spillover effects, enhancing the attractiveness of region to external production factors and thereby promoting urbanization 30 . However, compared to the secondary sector, the tertiary sector contributes less directly to CO 2 emissions. Instead, it influences emissions indirectly by promoting industrial upgrading and technological progress, making it a suitable instrumental variable for urbanization 31 . This study employs the two-stage least squares (2SLS) and two-stage predictor substitution (2SPS) methods to test the validity of the instrumental variables 32 . And then, we apply two-stage residual inclusion (2SRI) methods to estimate the relationship between PAG , URB , and CO 2 emissions. The 2SLS method addresses endogeneity in linear models through a two-stage regression process. In the first stage, the endogenous variables are regressed on the instrumental variables using a linear regression model. We apply a fixed-effects model with city-clustered robust standard errors for this regression (Eq. 4 ). In the second stage, the dependent variables are regressed on the fitted values of the endogenous variables from the first stage, along with the remaining exogenous variables (Eq. 5 ). $$\:\text{ln}{\text{X}}_{\text{e}}\text{=}\text{ln}{\text{a}}_{\text{1}}\text{+}{\text{b}}_{\text{1}}\text{ln}\text{Z}\text{+}{\text{b}}_{\text{2}}\text{ln}{\text{X}}_{\text{o}}\text{+}\text{ln}{\text{e}}_{\text{1}}$$ 4 $$\:\text{ln}{\text{CO}}_{\text{2}}\text{=}\text{ln}{\text{a}}_{\text{2}}\text{+}{\text{b}}_{\text{3}}\text{ln}{\widehat{\text{X}}}_{\text{e}}\text{+}{\text{b}}_{\text{4}}\text{ln}{\text{X}}_{\text{o}}\text{+}\text{ln}{\text{e}}_{\text{2}}$$ 5 Where, X e represents the endogenous variables, defined as X e = [ PAG URB ]. Z represents the instrumental variables, defined as Z = [ GEN TER ]. X o represents the exogenous control variables, defined as X o = [ PGDP EDU STR INS ]. The 2SPS method is an extension of 2SLS method for nonlinear models 33 . In the first stage, the endogenous variables are regressed nonlinearly on the instrumental variables (Eq. 6 ). In the second stage, the dependent variables are regressed nonlinearly on the fitted values of the endogenous variables from the first stage, along with the remaining exogenous variables (Eq. 7 ). $$\:\text{ln}{\text{X}}_{\text{e}}\text{=}\text{M}\left(\text{ln}{\text{a}}_{\text{1}}\text{+}{\text{b}}_{\text{1}}\text{ln}\text{Z}\text{+}{\text{b}}_{\text{2}}\text{ln}{\text{X}}_{\text{o}}\text{+}\text{ln}{\text{e}}_{\text{1}}\right)$$ 6 $$\:\text{ln}{\text{CO}}_{\text{2}}\text{=M}\left(\text{ln}{\text{a}}_{\text{2}}\text{+}{\text{b}}_{\text{3}}\text{ln}{\widehat{\text{X}}}_{\text{e}}\text{+}{\text{b}}_{\text{4}}\text{ln}{\text{X}}_{\text{o}}\text{+}\text{ln}{\text{e}}_{\text{2}}\right)$$ 7 We utilize Nonlinear Least Squares (NLS) for regression analysis in this section. Here, M (.) represents the nonlinear model, while the definitions of other symbols remain consistent with those provided earlier. The 2SRI method was first introduced by Hausman (1978) 34 . Terza et al. (2008) 35 demonstrated through mathematical modeling and data simulation that the 2SRI helps correct estimation bias caused by endogenous variables. In the first stage, the endogenous variables are regressed nonlinearly on the instrumental variables (Eq. 6 ). In the second stage, the dependent variables are regressed nonlinearly on the fitted residuals from the first stage, along with the remaining exogenous variables (Eq. 8 – 9 ). $$\:\text{ln}{\widehat{\text{e}}}_{\text{1}}\text{=}\text{ln}{\text{X}}_{\text{e}}\text{-}\text{ln}{\text{a}}_{\text{1}}\text{-}{\text{b}}_{\text{1}}\text{ln}\text{Z}\text{-}{\text{b}}_{\text{2}}\text{ln}{\text{X}}_{\text{o}}$$ 8 $$\:\text{ln}{\text{CO}}_{\text{2}}\text{=}\text{ln}{\text{a}}_{\text{2}}\text{+}{\text{b}}_{\text{3}}\text{ln}{\text{X}}_{\text{e}}\text{+}{\text{b}}_{\text{4}}\text{ln}{\text{X}}_{\text{o}}\text{+}{\text{b}}_{\text{5}}\text{ln}{\widehat{\text{e}}}_{\text{1}}\text{+}\text{ln}{\text{e}}_{\text{2}}$$ 9 This section also employs NLS for coefficient estimation. Where \(\:\text{ln}{\widehat{e}}_{1}\) represents the fitted residual values. The coefficient b 5 is used to test whether there is a significant difference in the estimated coefficient of the endogenous variable b 3 , between models that control for \(\:\text{ln}{\widehat{e}}_{1}\) and those that do not 36 . The specific testing process is outlined as in Eq. 10 – 11 . The relationship between the endogenous variable, the residual terms, instrumental variables and their covariates is formalized in Eq. 10 . $$\:\text{ln}{\text{X}}_{\text{e}}\text{=}{\text{c}}_{\text{1}}\text{ln}\text{Z}\text{+}{\text{c}}_{\text{2}}\text{ln}\text{W}\text{+}\text{ln}\text{ω}$$ 10 The residual term in Eq. 9 can be expressed as Eq. 11 . $$\:\text{ln}\text{e}\text{=}{\text{b}}_{\text{5}}\left({\text{c}}_{\text{2}}\text{ln}\text{W}\text{+}\text{ln}\text{ω}\right)\text{+}\text{ln}{\text{e}}_{\text{2}}$$ 11 Where, W represents the covariates of the instrumental variables, satisfying \(\:E\left({Z}^{{\prime\:}}W\right)\ne\:0\) . ω denotes the residual term. Besides the instrumental variable Z , other related variables jointly influence the endogenous variable. As a result, the estimate coefficient c 1 in Eq. 10 differs from the estimated coefficient b 1 in Eq. 4 , leading to estimation bias and causing b 3 and b 5 in Eq. 9 to approach zero. Conversely, if b 3 and b 5 do not approach zero, meaning that the estimation results are both significant, they are considered to downward-bound 37 . In other words, when both b 3 and b 5 are significant, the instrumental variables can be considered to meet the selecting conditions. Control Function Approach (CFA) This study analyzes the economic factors that significantly associate PAG and URB by using the control function approach (CFA) from income and expenditure dimensions. Income-related factors include fiscal revenue ( REV ) and per capita disposable income ( PINC ), both of which are associated with regional labor force agglomeration, as well as the growth of corporate and labor earnings 38 . This trend influences aging and urbanization processes by attracting population inflows, ultimately impacting CO 2 emissions. Expenditure-related factors include the share of tertiary sector in GDP ( TER ) and fixed-asset investment ( INV ), both of which are linked to local government investment in public service infrastructure, improvements in public infrastructure, enhanced public services, and greater business attractiveness. This process alters aging and urbanization by improving the quality of life for the elderly and promoting industrial upgrading, ultimately impacting CO 2 emissions. The CFA method in this study is used to examine the relationship between the explanatory variables within the STIRPAT model and external exogenous variables 36 . In the first stage, PAG and URB are separately regressed on economic factors (Eq. 12 ). In the second stage, CO 2 emissions are regressed on the fitted residuals from the first stage (Eq. 13 ). Since other economic factors are highly correlated with PGDP , which may cause multicollinearity, PGDP is excluded from the model in this section. $$\:\text{ln}\text{P}\text{=}\text{ln}{\text{a}}_{\text{1}}\text{+}{\text{b}}_{\text{1}}\text{ln}\text{ECO}\text{+}{\text{b}}_{\text{2}}\text{ln}\text{T}\text{+}\text{ln}{\text{e}}_{\text{1}}$$ 12 $$\:\text{ln}{\text{CO}}_{\text{2}}\text{=}\text{ln}{\text{a}}_{\text{2}}\text{+}{\text{b}}_{\text{3}}\text{ln}\text{P}\text{+}{\text{b}}_{\text{4}}\text{ln}\text{T}\text{+}{\text{b}}_{\text{5}}\text{ln}{\widehat{\text{e}}}_{\text{1}}\text{+}\text{ln}{\text{e}}_{\text{2}}$$ 13 Where, P represents population aging and urbanization, defined as P = [ PAG URB ]. ECO represents economic factors, defined as ECO = [ REV PINC TER INV ]. T represents technological progress factors, defined as T = [ EDU STR INS ]. The coefficient b 5 indicates the correlation between population aging or urbanization and CO 2 emissions under the influence of specific economic variables. Multi-scenario CO 2 emissions prediction Based on the Shared Socioeconomic Pathways (SSPs) proposed in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) 14 , this study further predicts CO 2 emissions in China cities from 2030 to 2060 under the SSP1-SSP5 scenarios. Specifically, SSP1 represents a sustainable development pathway, characterized by effective control of population growth and improvements in overall population quality. SSP2 reflects a continuation of current policies, with both population growth and economic development following existing trends. SSP3 represents a regional rivalry pathway, characterized by a growing total population and a higher birth rate compared to other scenarios, resulting in a lower proportion of the elderly individuals. SSP4 describes an inequality pathway, in which regional disparities lead to uneven patterns of population aging and urbanization. SSP5 represents a fossil fuel-driven development pathway, characterized by relatively stable population growth alongside rapid, balanced urbanization and population aging. Based on the estimated coefficients in Eq. 3 and projected variable values, this study predicts CO 2 emissions in Chinese cities from 2030 to 2060. To assess the combined effects of aging and urbanization on CO₂ emissions, we subtract the CO 2 emissions estimated under the assumption that population aging and urbanization remain at their 2020 levels (with all other variables set to their 2060 projections) from the emissions estimated using 2060 projections for all variables. Statistics and reproducibility This study uses a sample of 200 observations for statistical analysis (n=200). Descriptive statistics of the dataset, including the mean, standard deviation, median, maximum, and minimum values, are presented in Appendix Table S3. This study conducts a series of statistical tests to evaluate the variables setting and coefficient estimates of the empirical model. Specifically, multicollinearity is assessed using the mean Variance Inflation Factor (VIF). The Hausman test is applied to determine the appropriateness of Fixed Effects Model (FE) versus Random Effects Model (RE) and to examine the endogeneity of population aging and urbanization. The Kleibergen-Paap rk LM statistic is used to assess underidentification of the instrumental variables, while the Cragg-Donald Wald F statistic and the Kleibergen-Paap rk Wald F statistic are employed to test for weak identification. As only one instrumental variable is specified for both aging and urbanization, the model is exactly identified, and an overidentified test is therefore unnecessary. Detailed test results are reported in Appendix Table S4. This study sets the significance levels (α) at 0.01, 0.05, and 0.1. Two-tailed tests are conducted using non-directional alternative hypothesis. The t-test or z-test was used to generate P value. If the P valueα, we fail to reject the null hypothesis, suggesting that the independent variable does not have a statistically significant effect on the dependent variable. Detailed results of the significance tests and the actual P value are provided in Table 1 and Table 2. Declarations Data availability The CO 2 emissions used in this study are derived from the CEADs database, https://www.ceads.net.cn/. The regression data used in the empirical model are obtained from the following publicly available sources: https://www.ceads.net.cn/; https://www.stats.gov.cn/sj/ndsj/; https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2022040095?z=Z012; https://www.stats.gov.cn/sj/pcsj/rkpc/7rp/zk/indexch.htm, with detailed references listed in Supplementary Table 1. The predictions of population aging, urbanization, per capita GDP, and education in this study have been deposited in Scientific Data at https://doi.org/10.6084/m9.figshare.c.4605713.v1, and Science Data Bank at https://doi.org/10.57760/sciencedb.01683. The predictions of energy structure and energy instance in this study are available within the article and its Supplementary Table 2. The projected city-level CO 2 emissions, including those associated with population aging and urbanization, that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgements This study was funded by the National Nature Science Foundation of China (grant number 72104029, 72403022). Author contributions C.Z. and Y.C.Z. jointly conceived the study and designed the empirical model. Y.C.Z. prepared the data, performed the data analysis, and drafted the initial manuscript. M.Z.Z. and Y.C.Z. analyzed and interpreted the analytic model, and critically revised the manuscript for important content. All authors reviewed and approved the final version of the manuscript. C.Z. serves as the guarantor. The corresponding authors affirm that all listed authors meet the authorship criteria and that no eligible contributors have been omitted. Competing interests All authors declare no financial or non-financial competing interests. References UN. Population Division. World Population Prospects 2022: Summary of Results (United Nations, 2022). UN. Population Division. World Urbanization Prospects 2018: Highlights (United Nations, 2019). 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Ala-Mantila, S., Heinonen, J. & Junnila, S. Relationship Between Urbanization, Direct and Indirect Greenhouse Gas Emissions, and Expenditures: A Multivariate Analysis. Ecolog. Econ. 104 , 129-139 (2014). Wang, Q., Wang, X. & Li, R. Does Population Aging Reduce Environmental Pressures from Urbanization in 156 Countries? Science of the Total Environment 848 , 157330 (2022). Yang, L. Analysis of the Limitations of the Petty-Clark Theorem in Economically Underdeveloped Regions of Western China (in Chinese). Inquiry into Economic Issues , 18-23 (2001). Chen, Y. et al. Provincial and Gridded Population Projection for China Under Shared Socioeconomic Pathways from 2010 to 2100 Data sets. Scientific Data https://doi.org/10.6084/m9.figshare.c.4605713.v1 (2020). Pao, H. & Tsai, C. Modeling and Forecasting the CO₂ Emissions, Energy Consumption, and Economic Growth in Brazil. Energy 36 , 2450-2458 (2011). Tonn, B. & Eisenberg, J. The Aging US Population and Residential Energy Demand. Energy Policy 35 , 743-745 (2007). Wang, Q., Li, L. & Li, R. Uncovering the Impact of Income Inequality and Population Aging on Carbon Emission Efficiency: An Empirical Analysis of 139 Countries. Science of The Total Environment 857 , 159508 (2023). Wang, W., Liu, L., Liao, H. & Wei, Y. Impacts of Urbanization on Carbon Emissions: An Empirical Analysis from OECD Countries. Energy Policy 151 , 112171 (2021). Sheng, P. & Guo, X. The Long-Run and Short-Run Impacts of Urbanization on Carbon Dioxide Emissions. Econ. Modelling 53 , 208-215 (2016). Yu, R. et al. Does Population Aging Affect Carbon Emission Intensity by Regulating Labor Allocation? Sustainability 15 , 9721 (2023). Dietz, T. & Rosa, E. A. Effects of Population and Affluence on CO₂ Emissions. Proceedings of the National Academy of Sciences 94 , 175-179 (1997). Ehrlich, P. R. & Holdren, J. P. Impact of Population Growth: Complacency Concerning This Component of Man's Predicament Is Unjustified and Counterproductive. Science 171 , 1212-1217 (1971). York, R., Rosa, E. A. & Dietz, T. STIRPAT, IPAT, and ImPACT: Analytic Tools for Unpacking the Driving Forces of Environmental Impacts. Ecolog. Econ. 46 , 351-365 (2003). Dietz, T., Rosa, E. A. & York, R. Driving the Human Ecological Footprint. Frontiers in Ecology and the Environment 5 , 13-18 (2007). Zhang, Z. & Li, Q. Population Aging Caused by a Rise in the Sex Ratio at Birth. Demographic Research 43 , 969-992 (2020). Zhang, X. & Zhang, X. The Role of the Internal Levels of Tertiary Industry in Urbanization Development — Based on Panel Data of Five Provinces (In Chinese). Economic Survey 33 , 25-30 (2016). Jiang, T., Yang, J. & Huang, S. Evolution and driving factors of CO₂ emissions structure in China's heating and power industries: The supply-side and demand-side dual perspectives. Journal of Cleaner Production 264 (2020). Leng, C. & Zhu, Z. Housing Security and the Happiness of Residents: Evidence from the China General Social Survey (In Chinese). China Economic Studies , 82 (2021). Lu, C. & Hongli, F. Does Family Elderly Care Reduce Female Employment? A Two-Stage Residual Inclusion Approach (in Chinese). Population Research 40 , 71-81 (2016). Hausman, J. A. Specification Tests in Econometrics. Econometrica: Journal of the econometric society 46 , 1251-1271 (1978). Terza, J. V., Basu, A. & Rathouz, P. J. Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling. J. Health Econ. 27 , 531-543 (2008). Castells Quintana, D., del Pilar Lopez Uribe, M. & McDermott, T. K. Population Displacement and Urban Conflict: Global Evidence From More Than 3,300 Flood Events. J. Devel. Econ. 158 , 102922 (2022). Castells Quintana, D. & Royuela, V. Tracking Positive and Negative Effects of Inequality on Long-Run Growth. Empirical Econ. 53 , 1349-1378 (2017). Liu, M. & Feng, H. How Tax Structure Affects Government Scale? A New Interpretation Based on Economic Growth Model (In Chinese). Contemporary Finance & Economics 4 , 32-43 (2016). Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in npj Urban Sustainability → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6852089","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":506486638,"identity":"89237563-1998-46ab-ab6a-dd928da9202b","order_by":0,"name":"Yongchun Zhao","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yongchun","middleName":"","lastName":"Zhao","suffix":""},{"id":506486639,"identity":"0aa28559-8d0d-4cb6-acca-87fb411c5372","order_by":1,"name":"Mengzhen Zhao","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mengzhen","middleName":"","lastName":"Zhao","suffix":""},{"id":506486640,"identity":"10538259-b8b7-47c9-9e67-4ffe1b0ad975","order_by":2,"name":"Chi Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCTB5gIdfgrEByGAmQYvkDMbGBpK0MBjcYGAkTov87OZnD7/U3JExvt3c/oChwjqxgf3sAbxaGOccMzeWOfaMx+zOQaDDzqQnNvDkJeDVwiyRYCYt2XCYx+xGYmMDY9vhxAYJHgO8Wtgk0r+BtRjPAGn5R4QWHokcM8mPQC0GEiAtDURokZDIKZNmOHaYRwLolxkJx9KN23hy8GuRn5G+TfJHzWF7/tntDz58qLGW7Wc/g18LCDDzwFgJIN8RVA8EjD+IUTUKRsEoGAUjFwAA9LJGqpe2WEQAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Chi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-09 08:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6852089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6852089/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42949-025-00316-7","type":"published","date":"2025-12-26T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90433385,"identity":"18a404d1-8ef5-4e14-88bd-c9869b7373c4","added_by":"auto","created_at":"2025-09-02 16:10:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":288524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportions of CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions associated with population aging and urbanization in total CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe impact of population aging is represented by the proportion of CO\u003csub\u003e2\u003c/sub\u003e emissions related to to it in the total CO\u003csub\u003e2\u003c/sub\u003e emissions, shown as blue bars. Given the positive relationship between population aging and CO\u003csub\u003e2\u003c/sub\u003e emissions, the related emissions are greater than zero. The impact of urbanization is represented by the proportion of CO\u003csub\u003e2\u003c/sub\u003e emissions related to to it in the total CO\u003csub\u003e2\u003c/sub\u003e emissions, shown as red bars. Given the negative relationship between urbanization and CO\u003csub\u003e2\u003c/sub\u003e emissions, the related emissions are less than zero.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6852089/v1/ac5def9a041978f2dcf9ce88.png"},{"id":90433714,"identity":"2e9d3e5a-baf8-487d-a0ac-de23c99d7a8f","added_by":"auto","created_at":"2025-09-02 16:18:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1733253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions associated with population aging and urbanization in 2060.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProjected data for SSP1 to SSP5 in 2060 are shown in the figure. The figure shows the projection results under five scenarios (SSP1 to SSP5), proportionally categorized into five levels. Specifically, the dark green level represents the smallest combined effects of population aging and urbanization on total CO\u003csub\u003e2\u003c/sub\u003e emissions, followed by light green, yellow, and orange. The bright red level indicates the highest combined effects. Areas in white on the map indicate regions with no available data.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6852089/v1/92527d821ee4eb933b9ae4df.png"},{"id":99172241,"identity":"ec44009f-7221-43b0-845b-b528490be30d","added_by":"auto","created_at":"2025-12-29 16:05:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3189433,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6852089/v1/adb3494b-fd08-4b2a-8787-0befb0887581.pdf"},{"id":90433386,"identity":"ecd73ce0-7204-4212-bee0-943f855bacba","added_by":"auto","created_at":"2025-09-02 16:10:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54508,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6852089/v1/641e6061db405b4dcded7959.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Impact of Aging and Urbanization on CO\u003csub\u003e2\u003c/sub\u003e Emission in Chinese Cities: An Empirical Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRising global aging and urbanization pose significant challenges to carbon reductions and achieve Paris Agreement targets. According to the World Population Prospects 2022\u003csup\u003e1\u003c/sup\u003e, the global population aged 65 and above is increasing faster than that under 65, and projecting to rise from 10% in 2022 to 16% by 2050. Meanwhile, the World Urbanization Prospects 2018\u003csup\u003e2\u003c/sup\u003e reports that 55% of the global population are urban dwellers, and projects to reach 68% by 2050, with most growth concentrated in Asia and Africa. China is one of the fastest-aging and urbanizing countries in the world over the past and coming decade\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, with these shifts posing significant challenges to achieve carbon neutrality by 2060. It is crucial for China to clarify the combined effects of population aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions and to formulate targeted mitigation strategies in response. In reality, urbanization reshapes the age structure of urban and rural populations, while aging influences the pace and quality of future urbanization\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These interactions highlight the importance of integrating aging and urbanization within a unified analytical framework to assess their combined effects on CO\u003csub\u003e2\u003c/sub\u003e emissions. However, existing studies generally examined the directly impact of population aging or urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions in isolation. An aging society increased demand for healthcare and assisted living industries\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e while also driving up energy consumption for heating, cooking, and public transportation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Urban residents rely less on biomass fuels for heating and cooking, resulting in lower household CO\u003csub\u003e2\u003c/sub\u003e emissions compared to rural areas\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Particularly in regions with high production efficiency, well-developed information networks, and advanced technological adaptation, urbanization leveraged economies of scale to promote population and industrial agglomeration\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, thereby directly improving energy efficiency and reducing CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\u003cp\u003eWhile the impacts of population aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emission are largely driven by regional economic conditions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, limited research has explored the specific economic factors that influence these demographic trends and, in turn, affect CO\u003csub\u003e2\u003c/sub\u003e emissions. Existing studies on economic-driven factors have shown that lower-income elderly populations tended to adopt less on environmentally friendly consumption habits, resulting in higher CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Conversely, wealthier households, having greater financial resources and choices, prefer to use cleaner energy sources like electricity and natural gas\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, Chikaraishi et al. (2015)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e found that higher per capita GDP and a greater service-sector share contribute to more environmentally sustainable urbanization. Collectively, these findings highlight the academic importance of exploring the economic drivers of demographic changes and the resulting impacts of those changes on CO\u003csub\u003e2\u003c/sub\u003e emissions, as closing this research gap is essential for informing effective, economically grounded carbon reduction policies. Furthermore, while existing studies have predicted future CO\u003csub\u003e2\u003c/sub\u003e emissions trends related to population aging and urbanization at the national\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e or provincial level\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, they often overlook city-level forecasts, limiting the ability to reveal regional differences in China\u0026rsquo;s future emissions.\u003c/p\u003e\u003cp\u003eThis study empirically examines the combined effects of population aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions by (a) constructing a city-level STIRPAT model that accounts for the endogeneity of aging and urbanization; (b) applying the Control Function Approach (CFA) to analyze correlations among economic factors; and (c) projecting city-level CO\u003csub\u003e2\u003c/sub\u003e emissions in China from 2030 to 2060 under the five SSP scenarios (SSP1-SSP5) defined in the IPCC Sixth Assessment Report\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions impacts of aging and urbanization\u003c/h2\u003e\n \u003cp\u003eUsing city-level data from 2010 to 2020 in China, we conduct an empirical analysis of the combined effects of population aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions. The result shows that (Table 1), China\u0026rsquo;s CO\u003csub\u003e2\u003c/sub\u003e emissions exhibited an upward trend, with aging and urbanization exert opposing effects. Specifically, the results obtained from the instrumental variable (IV) approach indicate a positive relationship between population aging (\u003cem\u003ePAG\u003c/em\u003e) and CO\u003csub\u003e2\u003c/sub\u003e emissions, and a negative relationship between urbanization (\u003cem\u003eURB\u003c/em\u003e) and CO\u003csub\u003e2\u003c/sub\u003e emissions. Table 1 reports the estimates under both exogeneity and endogeneity assumptions, using fixed effects, random effects, 2SLS, 2SPS and 2SRI models. Based on the test outcomes, we select the 2SRI model for detailed regression analysis. Supplementary Table 4 presents the detail results of tests for multicollinearity, endogeneity, and the validity of the instrumental variables.\u003c/p\u003e\n \u003cp\u003eThe 2SRI regression results show that (Table 1), \u003cem\u003ePAG\u003c/em\u003e is significantly positively associated with CO\u003csub\u003e2\u003c/sub\u003e emissions at the 1% significance level, with a one-unit increase in aging is expected to raise CO\u003csub\u003e2\u003c/sub\u003e emissions by 3.007 units (e\u003csup\u003e1.101\u003c/sup\u003e=3.016). \u003cem\u003eURB\u003c/em\u003e is significantly negatively associated with CO\u003csub\u003e2\u003c/sub\u003e emissions at the 5% significance level, with a one-unit increase in urbanization expected to reduce emissions by 0.409 units (e\u003csup\u003e\u0026minus;\u0026thinsp;0.893\u003c/sup\u003e=0.409). Column 5 shows that the first-stage residuals are statistically significant in CO\u003csub\u003e2\u003c/sub\u003e emissions at the 1% and 10% levels, indicating that the validity of the instrumental variables and the appropriateness of the 2SRI estimation method. The finding that population aging is positively associated with CO\u003csub\u003e2\u003c/sub\u003e emissions is consistent with Yu et al. (2018)\u003csup\u003e7\u003c/sup\u003e. Similarly, the negative association between urbanization and CO\u003csub\u003e2\u003c/sub\u003e emissions is consistent with Ala-Mantila et al. (2014)\u003csup\u003e15\u003c/sup\u003e, which argues that, given equal levels of consumption and expenditure, urban lifestyles result in less environmental damage than rural ones.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparative regression results of aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eExogenous hypothesis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEndogenous hypothesis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003cp\u003eFE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cp\u003e2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003cp\u003e2SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003cp\u003e2SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.126***\u003c/p\u003e\n \u003cp\u003e(-4.116)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.085***\u003c/p\u003e\n \u003cp\u003e(-2.699)\u003c/p\u003e\n \u003cp\u003e[0.007]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.057***\u003c/p\u003e\n \u003cp\u003e(3.231)\u003c/p\u003e\n \u003cp\u003e[0.001]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.062***\u003c/p\u003e\n \u003cp\u003e(3.184)\u003c/p\u003e\n \u003cp\u003e[0.001]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.101***\u003c/p\u003e\n \u003cp\u003e(3.173)\u003c/p\u003e\n \u003cp\u003e[0.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003cp\u003e(-0.446)\u003c/p\u003e\n \u003cp\u003e[0.655]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003cp\u003e(-1.195)\u003c/p\u003e\n \u003cp\u003e[0.232]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003cp\u003e[0.986]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.676\u003c/p\u003e\n \u003cp\u003e(-1.527)\u003c/p\u003e\n \u003cp\u003e[0.127]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.893**\u003c/p\u003e\n \u003cp\u003e(-2.038)\u003c/p\u003e\n \u003cp\u003e[0.042]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275***\u003c/p\u003e\n \u003cp\u003e(17.960)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.270***\u003c/p\u003e\n \u003cp\u003e(17.389)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.239***\u003c/p\u003e\n \u003cp\u003e(2.869)\u003c/p\u003e\n \u003cp\u003e[0.004]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.326***\u003c/p\u003e\n \u003cp\u003e(3.718)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346***\u003c/p\u003e\n \u003cp\u003e(3.845)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006***\u003c/p\u003e\n \u003cp\u003e(5.145)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654***\u003c/p\u003e\n \u003cp\u003e(3.202)\u003c/p\u003e\n \u003cp\u003e[0.001]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.991***\u003c/p\u003e\n \u003cp\u003e(3.776)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.589***\u003c/p\u003e\n \u003cp\u003e(4.829)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.139***\u003c/p\u003e\n \u003cp\u003e(5.366)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144***\u003c/p\u003e\n \u003cp\u003e(7.013)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139***\u003c/p\u003e\n \u003cp\u003e(6.594)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229***\u003c/p\u003e\n \u003cp\u003e(7.856)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233***\u003c/p\u003e\n \u003cp\u003e(8.212)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233***\u003c/p\u003e\n \u003cp\u003e(7.860)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339***\u003c/p\u003e\n \u003cp\u003e(21.192)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.324***\u003c/p\u003e\n \u003cp\u003e(19.235)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429***\u003c/p\u003e\n \u003cp\u003e(20.191)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409***\u003c/p\u003e\n \u003cp\u003e(18.014)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402***\u003c/p\u003e\n \u003cp\u003e(18.021)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.472\u003c/p\u003e\n \u003cp\u003e(-1.091)\u003c/p\u003e\n \u003cp\u003e[0.275]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003cp\u003e(0.887)\u003c/p\u003e\n \u003cp\u003e[0.375]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.453***\u003c/p\u003e\n \u003cp\u003e(-6.311)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.420***\u003c/p\u003e\n \u003cp\u003e(-6.806)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.892***\u003c/p\u003e\n \u003cp\u003e(-7.000)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResiduals from PAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.437***\u003c/p\u003e\n \u003cp\u003e(-4.063)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResiduals from URB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.817*\u003c/p\u003e\n \u003cp\u003e(1.796)\u003c/p\u003e\n \u003cp\u003e[0.072]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eobs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: The values in parentheses in Columns 1 and 2 represent t-values, while those in Columns 3 to 5 correspond to z-values. The values in square brackets represent p-values. Significance levels (2-tailed) are denoted as follows: * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eTable legend\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eColumns 1 and 2 report the results assuming that all variables are exogenous, while columns 3\u0026ndash;5 display the coefficient estimates obtained using 2SLS, 2SPS, and 2SRI, respectively, under the assumption that both \u003cem\u003ePAG\u003c/em\u003e and \u003cem\u003eURB\u003c/em\u003e are endogenous variables. Unlike the results in columns 1 and 2, the consistent regression results showed in columns 3\u0026ndash;5 indicate a positive relationship between \u003cem\u003ePAG\u003c/em\u003e and CO\u003csub\u003e2\u003c/sub\u003e emissions. Additionally, the results in columns 4 and 5 show a negative relationship between \u003cem\u003eURB\u003c/em\u003e and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eThe influence of economic factors on aging, urbanization, and CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/h3\u003e\n\u003cp\u003eThe existing literatures suggested that regional economic development closely drives population aging and urbanization\u003csup\u003e16\u003c/sup\u003e. We further employ the CFA method to examine how economic factors drive the effects of aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions in China.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Table 2a, columns 1\u0026ndash;4 present the first-stage regression results, showing that fiscal revenue (\u003cem\u003eREV\u003c/em\u003e), per capita disposable income (\u003cem\u003ePINC\u003c/em\u003e), and the share of the tertiary sector (\u003cem\u003eTER\u003c/em\u003e) are all significantly associated with \u003cem\u003ePAG\u003c/em\u003e. Specifically, \u003cem\u003eREV\u003c/em\u003e is significantly negatively correlated with \u003cem\u003ePAG\u003c/em\u003e at the 1% significance level, while \u003cem\u003ePINC\u003c/em\u003e is significantly negatively correlated with \u003cem\u003ePAG\u003c/em\u003e at the 5% significance level. A possible explanation is that higher fiscal revenue and Per capita disposable income enhance a city\u0026rsquo;s attractiveness to migrant workers, leading to an inflow of labor that reduces the proportion of the elderly population. In contrast, \u003cem\u003eTER\u003c/em\u003e is significantly positively correlated with \u003cem\u003ePAG\u003c/em\u003e at the 1% significance level. According to the Petty-Clark theorem\u003csup\u003e17\u003c/sup\u003e, as economic development progresses and per capita national income increases, the service sector gradually becomes the primary driver of economic growth. The empirical results suggest that during economic growth and industrial restructuring, if the total urban population remains constant, population aging will increase alongside economic expansion. Column 6 shows that \u003cem\u003ePAG\u003c/em\u003e driven by \u003cem\u003ePINC\u003c/em\u003e is significantly positively correlated with CO\u003csub\u003e2\u003c/sub\u003e emissions at the 1% significance level. Column 7 indicates that \u003cem\u003ePAG\u003c/em\u003e driven by \u003cem\u003eTER\u003c/em\u003e is also significantly positively correlated with CO\u003csub\u003e2\u003c/sub\u003e emissions at the 1% significance level.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Table 2b, columns 1 and 2 indicate that neither \u003cem\u003eREV\u003c/em\u003e nor \u003cem\u003ePINC\u003c/em\u003e is significantly correlated with \u003cem\u003eURB\u003c/em\u003e. Columns 3\u0026ndash;4 show that \u003cem\u003eTER\u003c/em\u003e and fixed asset investment (\u003cem\u003eINV\u003c/em\u003e) are both significantly negatively correlated with \u003cem\u003eURB\u003c/em\u003e at the 1% significance level. Column 5 indicates that \u003cem\u003eURB\u003c/em\u003e associated with \u003cem\u003eTER\u003c/em\u003e is significantly negatively correlated with CO\u003csub\u003e2\u003c/sub\u003e emissions at the 5% significance level. Based on these findings, this study concludes that per capita disposable income and the size of the tertiary sector are key drivers of population aging, which, in turn, is significantly positively correlated with CO\u003csub\u003e2\u003c/sub\u003e emissions. Additionally, the size of the tertiary sector is an important driver of urbanization, which, in turn, is significantly negatively associated with CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEstimation results for economic factors using the CFA method a. Estimation results of population aging effects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003cp\u003ePAG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIncome Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eREV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.077*** (-8.353)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128** (-2.387)\u003c/p\u003e\n \u003cp\u003e[0.017]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExpenditure Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTER\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088*** (3.962)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eINV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003cp\u003e(-0.666)\u003c/p\u003e\n \u003cp\u003e[0.506]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRevenue-driven population aging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003cp\u003e(-0.410)\u003c/p\u003e\n \u003cp\u003e[0.682]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePer capita disposable income-driven population aging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.003***\u003c/p\u003e\n \u003cp\u003e(15.264)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTertiary-driven population aging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.198***\u003c/p\u003e\n \u003cp\u003e(18.569)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResiduals from column (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003cp\u003e(0.147)\u003c/p\u003e\n \u003cp\u003e[0.883]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResiduals from column (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.282***\u003c/p\u003e\n \u003cp\u003e(-16.257)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResiduals from column (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.503***\u003c/p\u003e\n \u003cp\u003e(-19.918)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eb. Estimation results of urbanization effects\u003c/p\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003cp\u003eURB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003cp\u003eURB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cp\u003eURB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003cp\u003eURB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIncome Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eREV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e(0.250)\u003c/p\u003e\n \u003cp\u003e[0.803]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003cp\u003e(0.842)\u003c/p\u003e\n \u003cp\u003e[0.400]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExpenditure Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTER\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.136***\u003c/p\u003e\n \u003cp\u003e(-6.138)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eINV\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054***\u003c/p\u003e\n \u003cp\u003e(-6.596)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTertiary-driven urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.872**\u003c/p\u003e\n \u003cp\u003e(-1.978)\u003c/p\u003e\n \u003cp\u003e[0.048]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInvestment-driven urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.091***\u003c/p\u003e\n \u003cp\u003e(-30.617)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResiduals from column (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.828*\u003c/p\u003e\n \u003cp\u003e(1.814)\u003c/p\u003e\n \u003cp\u003e[0.070]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResiduals from column (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.168***\u003c/p\u003e\n \u003cp\u003e(30.781)\u003c/p\u003e\n \u003cp\u003e[0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo. of cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: The values in parentheses in Columns 1\u0026ndash;4 represent t-values. In Columns 5\u0026ndash;7 of Table 5a and Columns 5\u0026ndash;6 of Table 5b, the values in parentheses represent z-values. The values in square brackets represent p-values. Significance levels (2-tailed) are denoted as follows: * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two tables separately present the relationship between economic factors (\u003cem\u003eREV\u003c/em\u003e, \u003cem\u003ePINC\u003c/em\u003e, \u003cem\u003eTER\u003c/em\u003e, \u003cem\u003eINV\u003c/em\u003e) and population aging, as well as between the same economic factors and urbanization. And then, we further illustrate how aging and urbanization, when significantly influenced by economic factors, relate to CO\u003csub\u003e2\u003c/sub\u003e emissions. This study controls for the remaining variables in the STIRPAT model, and since these control variables are not the focus of the analysis, their estimated coefficients are not presented in detail. The regression coefficients of the control variables are available from the authors upon request.\u003c/p\u003e\n\u003ch3\u003eAging and urbanization-related CO\u003csub\u003e2\u003c/sub\u003e emissions from 2030 to 2060\u003c/h3\u003e\n\u003cp\u003eThe projected rapid growth of population aging and urbanization under all scenarios\u003csup\u003e18\u003c/sup\u003e is expected to pose significant challenges for China in achieving its carbon neutrality goal. Based on the coefficient estimation results from the empirical model, this study projects China\u0026rsquo;s future CO\u003csub\u003e2\u003c/sub\u003e emissions for 2030\u0026ndash;2060 at city level under different SSP scenarios. With a mean absolute percentage error (MAPE) of 19%, which falls within the 10\u0026ndash;20% range defined by Pao and Tsai (2011)\u003csup\u003e19\u003c/sup\u003e as indicative of good forecast accuracy, the use of STIRPAT regression coefficients to predict CO\u003csub\u003e2\u003c/sub\u003e emissions for 2030\u0026ndash;2060 is feasible.\u003c/p\u003e\n\u003cp\u003eThe combined effects of population aging and urbanization contribute to the overall growth of CO\u003csub\u003e2\u003c/sub\u003e emissions, with the positive impact of population aging on emissions in all projected scenarios outweighs the negative impact of urbanization. Among 200 Chinese cities, SSP3 records the lowest CO\u003csub\u003e2\u003c/sub\u003e emissions influenced by the combined effects of population aging and urbanization, with 4663.29 Mt in 2030. In contrast, SSP5 shows the highest emissions influenced by the combined effects, reaching 5807.18 Mt in 2030. Supplementary Table S5 reports the projected total CO\u003csub\u003e2\u003c/sub\u003e emissions in detail. Among all scenarios, Fig. 1 shows that SSP3 also has the lowest share of emissions influenced by the combined effects relative to total projected emissions, at 30.86% in 2030 and 48.32% in 2060, followed by SSP2, SSP4, and SSP5, while SSP1 has the highest proportion. Under SSP1, SSP4, and SSP5, the proportion of CO₂ emissions attributable to aging and urbanization is relatively similar, reaching 30.02%, 30.22%, and 29.94% respectively in 2030, and increasing significantly to 46.19%, 47.36%, and 45.43% by 2060. SSP1 exhibits the highest degree of population aging among all SSP scenarios\u003csup\u003e18\u003c/sup\u003e, with the positive impact of population aging on CO\u003csub\u003e2\u003c/sub\u003e emissions reaching 39.72% in 2030 and rising to 70.29% by 2060, indicating the greatest pressure for carbon emission growth. Conversely, the negative impact of urbanization is most pronounced under SSP5, accounting for 9.73% in 2030 and 24.46% in 2060, slightly higher than those under SSP1 and SSP4. Its highest level of urbanization among all scenarios\u003csup\u003e18\u003c/sup\u003e reflects the greatest potential for emission reduction.\u003c/p\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eCity-level CO\u003csub\u003e2\u003c/sub\u003e emissions disparities in 2060\u003c/h2\u003e\n \u003cp\u003eAlthough the relative contribution of CO\u003csub\u003e2\u003c/sub\u003e emissions associated with population aging and urbanization in total CO\u003csub\u003e2\u003c/sub\u003e emissions shows similar across SSP1, SSP4, and SSP5, this study further examines the absolute differences under various scenarios. Our findings suggest that the combined effects of aging and urbanization will lead to substantial variation in future CO\u003csub\u003e2\u003c/sub\u003e across cities, with marked regional disparities. Overall (Fig. 2), in 2060, CO\u003csub\u003e2\u003c/sub\u003e emissions associated with these demographic shifts are projected to be lower in cities along the southeastern coast and higher in cities located in the northwest and northeast regions. Under the SSP2 scenario, 35 cities, including Taiyuan, Zibo, and Weifang, fall into the lowest range of CO\u003csub\u003e2\u003c/sub\u003e emissions, where future CO\u003csub\u003e2\u003c/sub\u003e emissions are expected to be more influenced by urbanization than by population aging, suggesting a greater potential for achieving carbon reduction targets compared to other cities. Conversely, CO\u003csub\u003e2\u003c/sub\u003e emissions in the highest range under the SSP2 scenario include Changzhou, Yan\u0026rsquo;an, Shenyang, among a total of 40 cities. In these cities, future CO\u003csub\u003e2\u003c/sub\u003e emissions are expected to be more influenced by population aging rather than urbanization, leading to greater carbon emission pressures compared to other cities.\u003c/p\u003e\n \u003cp\u003eCompared to SSP2 scenario, some cities under the SSP1 scenario, such as Hengyang, Yingtan, and Linyi, are expected to transition from lower to higher emission levels, among a total of 39 cities. These cities are primarily located in central and northern China, suggesting that the green development pathway of SSP1 has improved living standards and life expectancy, which in turn has accelerated population aging and increased CO\u003csub\u003e2\u003c/sub\u003e emissions. Moreover, the projected CO\u003csub\u003e2\u003c/sub\u003e emissions under SSP1 in Lianyungang, Yancheng, Zhenjiang, and Yangzhou have decreased compared to those under SSP2, with these cities shifting from higher emission levels under SSP2 to lower ones under SSP1. This indicates that SSP1 has enhanced urban attractiveness, facilitated rural-to-urban migration, accelerated urbanization, and consequently reduced projected CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n \u003cp\u003eUnder the SSP3 scenario, the overall level of urbanization is higher compared to SSP2, leading to lower projected CO\u003csub\u003e2\u003c/sub\u003e emissions. Notably, no cities experience a transition from lower to higher emission levels under SSP3 relative to SSP2. A total of 137 cities, including Jian, Suqian, and Shaoyang, are projected to shift from higher CO\u003csub\u003e2\u003c/sub\u003e emissions levels under SSP2 to lower levels under SSP3. These cities are primarily located in eastern and central China, indicating that despite intensified regional competition and resource constraints under SSP3, they remain relatively attractive to populations, which in turn drives deeper urbanization and contributes to lower CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n \u003cp\u003eIn most cities, projected CO\u003csub\u003e2\u003c/sub\u003e emissions under SSP4 are higher than under SSP2. Fifty cities, including Suzhou, Xiangxi, and Suizhou, are expected to shift from lower emission levels under SSP2 to higher levels under SSP4. In these cities, population aging is larger compared to SSP2. Due to the regional disparities under SSP4, projected CO\u003csub\u003e2\u003c/sub\u003e emissions in many cities are lower than under SSP2. Fourteen cities such as Beihai, Bozhou, and Laibin are expected to shift from higher emission categories in SSP2 to lower ones in SSP4. This indicates that these cities may experience higher levels of urbanization than under SSP2, contributing to future reductions in CO\u003csub\u003e2\u003c/sub\u003e emissions associated with both population aging and urbanization.\u003c/p\u003e\n \u003cp\u003eThe total projected CO\u003csub\u003e2\u003c/sub\u003e emissions under SSP5 are higher than those under SSP2. This indicates that in the fossil fuel-driven, high-growth pathway of SSP5, the emission increases associated with population aging outweigh the reductions from urbanization. Seventy-five cities, including Huaihua, Xiangxi, and Qiandongnan, are projected to have higher CO₂ emissions under SSP5 than under SSP2, with a corresponding shift to higher emission categories. Meanwhile, a few cities show lower emissions under SSP5 compared to SSP2. These cities have experienced a significant influx of rural migrants, resulting in rapid urban population growth and a lower share of elderly residents. However, the resulting changes in CO\u003csub\u003e2\u003c/sub\u003e emissions are not substantial enough to shift their emission categories.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper integrates population aging and urbanization into a unified model, setting it apart from previous studies that analyze them separately or rely only on elderly samples to assess the impact of urbanization. We account for potential endogeneity between these two demographic shifts factors and CO\u003csub\u003e2\u003c/sub\u003e emissions, to obtain consistent estimates of the combined effects of aging and urbanization. The estimation results indicate that the positive effect of population aging on CO\u003csub\u003e2\u003c/sub\u003e emissions exceeds the negative effect of urbanization, suggesting that the accelerating aging process is reshaping emissions structure and diminishing the carbon reduction potential traditionally attributed to urbanization.\u003c/p\u003e\u003cp\u003eThere is a significant positive correlation between population aging and CO\u003csub\u003e2\u003c/sub\u003e emissions in Chinese cities, and this relationship is primarily driven by per capita disposable income and the share of the tertiary sector. As physical function declines with age, the elderly require higher indoor temperatures to ensure a healthy and comfortable living environment\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In China, coal remains the dominant fuel for household heating, and its use rises with the growing elderly population, contributing to higher carbon emissions associated with population aging. Moreover, nostalgia and conservative consumption habits reduce elderly\u0026rsquo;s motivation to purchase energy-efficient products\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. If the elderly population lives in older buildings with outdated infrastructure, it can further lead to inefficient use of energy for heating, cooling, thereby contributing to higher CO\u003csub\u003e2\u003c/sub\u003e emissions. Rising urban incomes attract more migrant labor, leading to population growth and a decline in the aging rate. However, rising income levels do not directly alter the consumption patterns of the elderly. Instead, the combined effect of higher incomes and greater purchasing power further intensifies the CO\u003csub\u003e2\u003c/sub\u003e emissions associated with population aging. On the other hand, the growth of tertiary industries such as technology and information services is able to support medical innovation and improve elderly care. This not only extends life expectancy and accelerates the aging trend but also stimulates new consumption patterns among the elderly, further contributing to rising CO\u003csub\u003e2\u003c/sub\u003e emissions pressures. As aging and urbanization increasingly interact in China, the effectiveness of traditional emission reduction strategies that focus solely on urbanization diminishes. Consequently, policymakers must integrate demographic factors into policy design and explore alternative approaches to emission reduction. This study suggests that the government promote low-carbon awareness among the elderly and encourage the adoption of energy-efficient products and services. Specific measures include increasing subsidies for low-carbon household appliances and expanding the range of eligible subsidized products, while also strengthening efforts to promote these low-carbon consumption activities among the elderly population. Meanwhile, the government and enterprises should enhance urban appeal to highly skilled labor by reasonably adjusting wage structures and steadily increasing income levels.\u003c/p\u003e\u003cp\u003eChina\u0026rsquo;s urbanization exhibited a significant negative correlation with CO\u003csub\u003e2\u003c/sub\u003e emissions, and this relationship is primarily driven by the share of the tertiary sector. We argue that urbanization affects emissions through both production and consumption behavior. On one hand, urbanization is accompanied by advancements in human capital and technology, which enhance energy efficiency and help to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. On the other hand, given urban residents\u0026rsquo; strong willingness to adopt low-carbon products and their widespread use of green appliances powered by renewable energy, further urbanization can effectively promote green consumption, thereby helping to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The growth of the tertiary sector generates new urban employment opportunities\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and typically involves lower energy consumption intensity. However, the shift toward industries distinct from labor-intensive or traditional agricultural sectors poses challenges for rural migrants entering cities, particularly regarding the skills and learning capacity required. This mismatch may lead to labor supply instability in the tertiary sector and, consequently, limit urban attractiveness to the rural population in China. Therefore, this study does not reveal a statistically significant positive correlation between the proportion of the tertiary sector and urbanization. Based on these findings, governments should offer skills training to support rural laborers meet the demands of emerging job opportunities, thereby improving the alignment between labor supply and market demand. At the same time, policy guidance on energy conservation for certain energy-intensive industries within the tertiary sector should be strengthened, with increased investment in energy-saving technologies and strict penalties imposed for illegal energy use.\u003c/p\u003e\u003cp\u003eFrom 2030 to 2060, the proportion of CO\u003csub\u003e2\u003c/sub\u003e emissions attributable to population aging in Chinese cities is projected to exceed that linked to urbanization. The positive effect of population aging on emissions and the negative effect of urbanization are both expected to intensify over time. The relative contribution of CO\u003csub\u003e2\u003c/sub\u003e emissions associated with population aging and urbanization in total CO\u003csub\u003e2\u003c/sub\u003e emissions remains similar across SSP1, SSP4, and SSP5. However, the absolute impact of the combined effects of aging and urbanization is projected to cause substantial variation in future CO\u003csub\u003e2\u003c/sub\u003e across cities. By 2060, cities with lower predicted CO\u003csub\u003e2\u003c/sub\u003e emissions related to population aging and urbanization will be primarily concentrated in central and southeastern China, while those with medium to high emissions will be mainly located in the northern and northeastern regions. The future trajectory of CO\u003csub\u003e2\u003c/sub\u003e emissions associated with population aging and urbanization will be shaped by the characteristics of projected scenarios, as well as by existing demographic and urban development patterns across cities. SSP1 and SSP5 exhibit significant aging trends, resulting in higher projected CO\u003csub\u003e2\u003c/sub\u003e emissions compared to SSP2. In contrast, SSP3 features accelerated urbanization, leading to lower emissions than SSP2. SSP4 presents a more complex picture, as some cities are projected to exceed SSP2 in emissions while others may fall below, reflecting a highly uneven distribution.\u003c/p\u003e\u003cp\u003eThe study has certain limitations that should be noted. CO\u003csub\u003e2\u003c/sub\u003e emissions projections for 2030\u0026ndash;2060 related to population aging and urbanization are based on regression coefficients derived from data spanning 2010 to 2020. This method does not account for cohort differences in savings and consumption preferences\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In particular, earlier-born elderly populations generally display lower consumption and higher savings tendencies, which are reflected in lower historical regression coefficients. Consequently, applying these historical coefficients may underestimate future CO\u003csub\u003e2\u003c/sub\u003e emissions associated with aging. Future research can further identify and analyze the evolving behavioral patterns of different elderly cohorts to improve the accuracy of long-term emissions projections.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBasic model foundation\u003c/h2\u003e\u003cp\u003eThis study employs the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, originally developed by Dietz and Rosa (1997)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e based on the IPAT Eq.\u0026nbsp;2\u003csup\u003e6\u003c/sup\u003e (Eq.\u0026nbsp;1), to empirically examine the correlation between aging, urbanization and CO\u003csub\u003e2\u003c/sub\u003e emissions. Compared to other models, the STIRPAT model is more suitable for examining the intrinsic relationship between human activities and environmental protection, as it offers greater flexibility to expand the model by adding, removing, or decomposing variables, thereby enhancing its analytical and explanatory capacity\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. To ensure data stationarity and facilitate computation, we transform the basic STIRPAT model into a logarithmic form\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (Eq.\u0026nbsp;2). Urbanization is then incorporated into the basic STIRPAT model (Eq.\u0026nbsp;3) to assess the combined impact with aging on CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{I}\\text{=}\\text{a}{\\text{P}}^{\\text{b}}{\\text{A}}^{\\text{c}}{\\text{T}}^{\\text{d}}\\text{e}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}\\text{I}\\text{=}\\text{ln}\\text{a}\\text{+}\\text{b}\\text{ln}\\text{P}\\text{+}\\text{c}\\text{ln}\\text{A}\\text{+}\\text{d}\\text{ln}\\text{T}\\text{+}\\text{ln}\\text{e}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}{\\text{CO}}_{\\text{2}}\\text{=}\\text{ln}\\text{a}\\text{+}{\\text{b}}_{\\text{1}}\\text{ln}\\text{PAG}\\text{+}{\\text{b}}_{\\text{2}}\\text{ln}\\text{URB}\\text{+}{\\text{b}}_{\\text{3}}\\text{ln}\\text{PGDP}\\text{+}{\\text{b}}_{\\text{4}}\\text{ln}\\text{EDU}\\text{+}{\\text{b}}_{\\text{5}}\\text{ln}\\text{STR}\\text{+}{\\text{b}}_{\\text{6}}\\text{ln}\\text{INS}\\text{+}\\text{ln}\\text{e}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003eI\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e, \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eT\u003c/em\u003e represent the environment factors, population, affluence, and technological progress, respectively. \u003cem\u003eCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ePAG\u003c/em\u003e, \u003cem\u003eURB\u003c/em\u003e, \u003cem\u003ePGDP\u003c/em\u003e, \u003cem\u003eEDU\u003c/em\u003e, \u003cem\u003eSTR\u003c/em\u003e, and \u003cem\u003eINS\u003c/em\u003e represent CO\u003csub\u003e2\u003c/sub\u003e emissions, population aging, urbanization, per capita GDP, average years of education per capita, energy structure, and energy intensity, respectively. \u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e, \u003cem\u003ec\u003c/em\u003e, and \u003cem\u003ed\u003c/em\u003e are parameters to be estimated, and \u003cem\u003ee\u003c/em\u003e represents the random disturbance term.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eAssessment of coefficient\u003c/h2\u003e\u003cp\u003eTo obtain consistent, unbiased, and efficient estimation, this study employs the Instrumental Variables (IV) method to address endogeneity issues. A valid instrumental variable must satisfy three conditions: (a) it must be strongly correlated with the endogenous variables, (b) it must be exogenous to the model, meaning uncorrelated with the random disturbance term, and (c) the number of instrumental variables must be at least equal to the number of endogenous variables. This study selects the gender ratio (\u003cem\u003eGEN\u003c/em\u003e) as the instrumental variable for \u003cem\u003ePAG\u003c/em\u003e. \u003cem\u003eGEN\u003c/em\u003e, defined as the number of males per 100 females, increases with a rise in the male population. It influences population size by affecting marriage and fertility rates, as well as migration driven by employment opportunities or marital considerations\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Moreover, \u003cem\u003eGEN\u003c/em\u003e has no significant direct impact on CO\u003csub\u003e2\u003c/sub\u003e emissions, reinforcing its suitability as an instrument. This study uses the share of tertiary sector in GDP (\u003cem\u003eTER\u003c/em\u003e) as the instrumental variable for \u003cem\u003eURB\u003c/em\u003e. Growth in the tertiary industry stimulates urban economic spillover effects, enhancing the attractiveness of region to external production factors and thereby promoting urbanization\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, compared to the secondary sector, the tertiary sector contributes less directly to CO\u003csub\u003e2\u003c/sub\u003e emissions. Instead, it influences emissions indirectly by promoting industrial upgrading and technological progress, making it a suitable instrumental variable for urbanization\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study employs the two-stage least squares (2SLS) and two-stage predictor substitution (2SPS) methods to test the validity of the instrumental variables\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. And then, we apply two-stage residual inclusion (2SRI) methods to estimate the relationship between \u003cem\u003ePAG\u003c/em\u003e, \u003cem\u003eURB\u003c/em\u003e, and CO\u003csub\u003e2\u003c/sub\u003e emissions. The 2SLS method addresses endogeneity in linear models through a two-stage regression process. In the first stage, the endogenous variables are regressed on the instrumental variables using a linear regression model. We apply a fixed-effects model with city-clustered robust standard errors for this regression (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the second stage, the dependent variables are regressed on the fitted values of the endogenous variables from the first stage, along with the remaining exogenous variables (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{X}}_{\\text{e}}\\text{=}\\text{ln}{\\text{a}}_{\\text{1}}\\text{+}{\\text{b}}_{\\text{1}}\\text{ln}\\text{Z}\\text{+}{\\text{b}}_{\\text{2}}\\text{ln}{\\text{X}}_{\\text{o}}\\text{+}\\text{ln}{\\text{e}}_{\\text{1}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{CO}}_{\\text{2}}\\text{=}\\text{ln}{\\text{a}}_{\\text{2}}\\text{+}{\\text{b}}_{\\text{3}}\\text{ln}{\\widehat{\\text{X}}}_{\\text{e}}\\text{+}{\\text{b}}_{\\text{4}}\\text{ln}{\\text{X}}_{\\text{o}}\\text{+}\\text{ln}{\\text{e}}_{\\text{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e represents the endogenous variables, defined as \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e = [\u003cem\u003ePAG URB\u003c/em\u003e]. \u003cem\u003eZ\u003c/em\u003e represents the instrumental variables, defined as \u003cem\u003eZ\u003c/em\u003e = [\u003cem\u003eGEN TER\u003c/em\u003e]. \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e represents the exogenous control variables, defined as \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e = [\u003cem\u003ePGDP EDU STR INS\u003c/em\u003e].\u003c/p\u003e\u003cp\u003eThe 2SPS method is an extension of 2SLS method for nonlinear models\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In the first stage, the endogenous variables are regressed nonlinearly on the instrumental variables (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the second stage, the dependent variables are regressed nonlinearly on the fitted values of the endogenous variables from the first stage, along with the remaining exogenous variables (Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{X}}_{\\text{e}}\\text{=}\\text{M}\\left(\\text{ln}{\\text{a}}_{\\text{1}}\\text{+}{\\text{b}}_{\\text{1}}\\text{ln}\\text{Z}\\text{+}{\\text{b}}_{\\text{2}}\\text{ln}{\\text{X}}_{\\text{o}}\\text{+}\\text{ln}{\\text{e}}_{\\text{1}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{CO}}_{\\text{2}}\\text{=M}\\left(\\text{ln}{\\text{a}}_{\\text{2}}\\text{+}{\\text{b}}_{\\text{3}}\\text{ln}{\\widehat{\\text{X}}}_{\\text{e}}\\text{+}{\\text{b}}_{\\text{4}}\\text{ln}{\\text{X}}_{\\text{o}}\\text{+}\\text{ln}{\\text{e}}_{\\text{2}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe utilize Nonlinear Least Squares (NLS) for regression analysis in this section. Here, \u003cem\u003eM\u003c/em\u003e(.) represents the nonlinear model, while the definitions of other symbols remain consistent with those provided earlier.\u003c/p\u003e\u003cp\u003eThe 2SRI method was first introduced by Hausman (1978)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Terza et al. (2008)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e demonstrated through mathematical modeling and data simulation that the 2SRI helps correct estimation bias caused by endogenous variables. In the first stage, the endogenous variables are regressed nonlinearly on the instrumental variables (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the second stage, the dependent variables are regressed nonlinearly on the fitted residuals from the first stage, along with the remaining exogenous variables (Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\widehat{\\text{e}}}_{\\text{1}}\\text{=}\\text{ln}{\\text{X}}_{\\text{e}}\\text{-}\\text{ln}{\\text{a}}_{\\text{1}}\\text{-}{\\text{b}}_{\\text{1}}\\text{ln}\\text{Z}\\text{-}{\\text{b}}_{\\text{2}}\\text{ln}{\\text{X}}_{\\text{o}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{CO}}_{\\text{2}}\\text{=}\\text{ln}{\\text{a}}_{\\text{2}}\\text{+}{\\text{b}}_{\\text{3}}\\text{ln}{\\text{X}}_{\\text{e}}\\text{+}{\\text{b}}_{\\text{4}}\\text{ln}{\\text{X}}_{\\text{o}}\\text{+}{\\text{b}}_{\\text{5}}\\text{ln}{\\widehat{\\text{e}}}_{\\text{1}}\\text{+}\\text{ln}{\\text{e}}_{\\text{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis section also employs NLS for coefficient estimation. Where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}{\\widehat{e}}_{1}\\)\u003c/span\u003e\u003c/span\u003e represents the fitted residual values. The coefficient \u003cem\u003eb\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e is used to test whether there is a significant difference in the estimated coefficient of the endogenous variable \u003cem\u003eb\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, between models that control for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}{\\widehat{e}}_{1}\\)\u003c/span\u003e\u003c/span\u003e and those that do not\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The specific testing process is outlined as in Eq.\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The relationship between the endogenous variable, the residual terms, instrumental variables and their covariates is formalized in Eq.\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{X}}_{\\text{e}}\\text{=}{\\text{c}}_{\\text{1}}\\text{ln}\\text{Z}\\text{+}{\\text{c}}_{\\text{2}}\\text{ln}\\text{W}\\text{+}\\text{ln}\\text{\u0026omega;}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe residual term in Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e9\u003c/span\u003e can be expressed as Eq.\u0026nbsp;\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}\\text{e}\\text{=}{\\text{b}}_{\\text{5}}\\left({\\text{c}}_{\\text{2}}\\text{ln}\\text{W}\\text{+}\\text{ln}\\text{\u0026omega;}\\right)\\text{+}\\text{ln}{\\text{e}}_{\\text{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003eW\u003c/em\u003e represents the covariates of the instrumental variables, satisfying \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({Z}^{{\\prime\\:}}W\\right)\\ne\\:0\\)\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eω\u003c/em\u003e denotes the residual term. Besides the instrumental variable \u003cem\u003eZ\u003c/em\u003e, other related variables jointly influence the endogenous variable. As a result, the estimate coefficient \u003cem\u003ec\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e in Eq.\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e10\u003c/span\u003e differs from the estimated coefficient \u003cem\u003eb\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e4\u003c/span\u003e, leading to estimation bias and causing \u003cem\u003eb\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e in Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e9\u003c/span\u003e to approach zero. Conversely, if \u003cem\u003eb\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e do not approach zero, meaning that the estimation results are both significant, they are considered to downward-bound\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In other words, when both \u003cem\u003eb\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e are significant, the instrumental variables can be considered to meet the selecting conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eControl Function Approach (CFA)\u003c/h2\u003e\u003cp\u003eThis study analyzes the economic factors that significantly associate \u003cem\u003ePAG\u003c/em\u003e and \u003cem\u003eURB\u003c/em\u003e by using the control function approach (CFA) from income and expenditure dimensions. Income-related factors include fiscal revenue (\u003cem\u003eREV\u003c/em\u003e) and per capita disposable income (\u003cem\u003ePINC\u003c/em\u003e), both of which are associated with regional labor force agglomeration, as well as the growth of corporate and labor earnings\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This trend influences aging and urbanization processes by attracting population inflows, ultimately impacting CO\u003csub\u003e2\u003c/sub\u003e emissions. Expenditure-related factors include the share of tertiary sector in GDP (\u003cem\u003eTER\u003c/em\u003e) and fixed-asset investment (\u003cem\u003eINV\u003c/em\u003e), both of which are linked to local government investment in public service infrastructure, improvements in public infrastructure, enhanced public services, and greater business attractiveness. This process alters aging and urbanization by improving the quality of life for the elderly and promoting industrial upgrading, ultimately impacting CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\u003cp\u003eThe CFA method in this study is used to examine the relationship between the explanatory variables within the STIRPAT model and external exogenous variables\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In the first stage, \u003cem\u003ePAG\u003c/em\u003e and \u003cem\u003eURB\u003c/em\u003e are separately regressed on economic factors (Eq.\u0026nbsp;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e12\u003c/span\u003e). In the second stage, CO\u003csub\u003e2\u003c/sub\u003e emissions are regressed on the fitted residuals from the first stage (Eq.\u0026nbsp;\u003cspan refid=\"Equ10\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Since other economic factors are highly correlated with \u003cem\u003ePGDP\u003c/em\u003e, which may cause multicollinearity, \u003cem\u003ePGDP\u003c/em\u003e is excluded from the model in this section.\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}\\text{P}\\text{=}\\text{ln}{\\text{a}}_{\\text{1}}\\text{+}{\\text{b}}_{\\text{1}}\\text{ln}\\text{ECO}\\text{+}{\\text{b}}_{\\text{2}}\\text{ln}\\text{T}\\text{+}\\text{ln}{\\text{e}}_{\\text{1}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$\\:\\text{ln}{\\text{CO}}_{\\text{2}}\\text{=}\\text{ln}{\\text{a}}_{\\text{2}}\\text{+}{\\text{b}}_{\\text{3}}\\text{ln}\\text{P}\\text{+}{\\text{b}}_{\\text{4}}\\text{ln}\\text{T}\\text{+}{\\text{b}}_{\\text{5}}\\text{ln}{\\widehat{\\text{e}}}_{\\text{1}}\\text{+}\\text{ln}{\\text{e}}_{\\text{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003eP\u003c/em\u003e represents population aging and urbanization, defined as \u003cem\u003eP\u003c/em\u003e = [\u003cem\u003ePAG URB\u003c/em\u003e]. \u003cem\u003eECO\u003c/em\u003e represents economic factors, defined as \u003cem\u003eECO\u003c/em\u003e = [\u003cem\u003eREV PINC TER INV\u003c/em\u003e]. \u003cem\u003eT\u003c/em\u003e represents technological progress factors, defined as \u003cem\u003eT\u003c/em\u003e = [\u003cem\u003eEDU STR INS\u003c/em\u003e]. The coefficient \u003cem\u003eb\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e indicates the correlation between population aging or urbanization and CO\u003csub\u003e2\u003c/sub\u003e emissions under the influence of specific economic variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMulti-scenario CO\u003csub\u003e2\u003c/sub\u003e emissions prediction\u003c/h2\u003e\u003cp\u003eBased on the Shared Socioeconomic Pathways (SSPs) proposed in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, this study further predicts CO\u003csub\u003e2\u003c/sub\u003e emissions in China cities from 2030 to 2060 under the SSP1-SSP5 scenarios. Specifically, SSP1 represents a sustainable development pathway, characterized by effective control of population growth and improvements in overall population quality. SSP2 reflects a continuation of current policies, with both population growth and economic development following existing trends. SSP3 represents a regional rivalry pathway, characterized by a growing total population and a higher birth rate compared to other scenarios, resulting in a lower proportion of the elderly individuals. SSP4 describes an inequality pathway, in which regional disparities lead to uneven patterns of population aging and urbanization. SSP5 represents a fossil fuel-driven development pathway, characterized by relatively stable population growth alongside rapid, balanced urbanization and population aging.\u003c/p\u003e\u003cp\u003eBased on the estimated coefficients in Eq.\u0026nbsp;3 and projected variable values, this study predicts CO\u003csub\u003e2\u003c/sub\u003e emissions in Chinese cities from 2030 to 2060. To assess the combined effects of aging and urbanization on CO₂ emissions, we subtract the CO\u003csub\u003e2\u003c/sub\u003e emissions estimated under the assumption that population aging and urbanization remain at their 2020 levels (with all other variables set to their 2060 projections) from the emissions estimated using 2060 projections for all variables.\u003c/p\u003e\u003c/div\u003e\u003ch2\u003eStatistics and reproducibility\u003c/h2\u003e\n\u003cp\u003eThis study uses a sample of 200 observations for statistical analysis (n=200). Descriptive statistics of the dataset, including the mean, standard deviation, median, maximum, and minimum values, are presented in Appendix Table S3.\u003c/p\u003e\n\u003cp\u003eThis study conducts a series of statistical tests to evaluate the variables setting and coefficient estimates of the empirical model. Specifically, multicollinearity is assessed using the mean Variance Inflation Factor (VIF). The Hausman test is applied to determine the appropriateness of Fixed Effects Model (FE) versus Random Effects Model (RE) and to examine the endogeneity of population aging and urbanization. The Kleibergen-Paap rk LM statistic is used to assess underidentification of the instrumental variables, while the Cragg-Donald Wald F statistic and the Kleibergen-Paap rk Wald F statistic are employed to test for weak identification. As only one instrumental variable is specified for both aging and urbanization, the model is exactly identified, and an overidentified test is therefore unnecessary. Detailed test results are reported in Appendix Table S4.\u003c/p\u003e\n\u003cp\u003eThis study sets the significance levels (\u0026alpha;) at 0.01, 0.05, and 0.1. Two-tailed tests are conducted using non-directional alternative hypothesis. The t-test or z-test was used to generate P value. If the P value\u0026lt;\u0026alpha;, the null hypothesis is rejected, indicating that the independent variable has a statistically significant impact on the dependent variable. Conversely, if P value\u0026gt;\u0026alpha;, we fail to reject the null hypothesis, suggesting that the independent variable does not have a statistically significant effect on the dependent variable. Detailed results of the significance tests and the actual P value are provided in Table 1 and Table 2.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e emissions used in this study are derived from the CEADs database, https://www.ceads.net.cn/. The regression data used in the empirical model are obtained from the following publicly available sources: https://www.ceads.net.cn/; https://www.stats.gov.cn/sj/ndsj/; https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2022040095?z=Z012; https://www.stats.gov.cn/sj/pcsj/rkpc/7rp/zk/indexch.htm, with detailed references listed in Supplementary Table 1. The predictions of population aging, urbanization, per capita GDP, and education in this study have been deposited in Scientific Data at https://doi.org/10.6084/m9.figshare.c.4605713.v1, and Science Data Bank at https://doi.org/10.57760/sciencedb.01683. The predictions of energy structure and energy instance in this study are available within the article and its Supplementary Table 2. The projected city-level CO\u003csub\u003e2\u003c/sub\u003e emissions, including those associated with population aging and urbanization, that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Nature Science Foundation of China (grant number 72104029, 72403022).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eC.Z. and Y.C.Z. jointly conceived the study and designed the empirical model. Y.C.Z. prepared the data, performed the data analysis, and drafted the initial manuscript. M.Z.Z. and Y.C.Z. analyzed and interpreted the analytic model, and critically revised the manuscript for important content. All authors reviewed and approved the final version of the manuscript. C.Z. serves as the guarantor. The corresponding authors affirm that all listed authors meet the authorship criteria and that no eligible contributors have been omitted.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUN. Population Division. World Population Prospects 2022: Summary of Results (United Nations, 2022).\u003c/li\u003e\n\u003cli\u003eUN. Population Division. World Urbanization Prospects 2018: Highlights (United Nations, 2019).\u003c/li\u003e\n\u003cli\u003eHan, X., Wei, C. \u0026amp; Cao, G. Aging, Generational Shifts, and Energy Consumption in Urban China. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, e2210853119 (2022).\u003c/li\u003e\n\u003cli\u003eWu, K. et al. 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A New Interpretation Based on Economic Growth Model (In Chinese). \u003cem\u003eContemporary Finance \u0026amp; Economics\u003c/em\u003e\u003cstrong\u003e4\u003c/strong\u003e, 32-43 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6852089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6852089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid aging and urbanization pose major challenges to global CO\u003csub\u003e2\u003c/sub\u003e reduction efforts, particularly in China. As such, effective carbon reduction strategies must account for their combined impact on emissions. However, existing research pays insufficient attention to the combined impact of aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions and the underlying economic drivers. Furthermore, city-level analyses for forecasting emissions related to these demographic shifts remain scarce. To address these limitations, this study constructs a STIRPAT model using city-level data from China in 2010\u0026ndash;2020 to estimate the combined effects of population aging and urbanization on CO\u003csub\u003e2\u003c/sub\u003e emissions. Then, we explore the economic drivers underlying this relationship. Finally, we project city-level CO\u003csub\u003e2\u003c/sub\u003e emissions driven by population aging and urbanization under different shared socioeconomic pathways (SSPs) in 2030\u0026ndash;2060. Our findings suggest that: (a) The emission-increasing effect of aging outweighed the mitigating impact of urbanization, and together these opposing forces contributed to the overall rise in emissions. (b) Per capita disposable income and the size of the tertiary sector are key economic drivers of population aging, contributing to increased CO\u003csub\u003e2\u003c/sub\u003e emissions. The tertiary sector also significantly influences urbanization, which in turn facilitates emission reductions. (c) The relative contribution of CO\u003csub\u003e2\u003c/sub\u003e emissions from population aging and urbanization is similar under SSP1, SSP4, and SSP5. However, the absolute levels of CO\u003csub\u003e2\u003c/sub\u003e emissions resulting from the combined effects of aging and urbanization exhibit significant variation across cities under all SSP1-SSP5 scenarios. Base on the results, we offer policy recommendations to support carbon mitigation efforts.\u003c/p\u003e","manuscriptTitle":"The Impact of Aging and Urbanization on CO2 Emission in Chinese Cities: An Empirical Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 16:10:00","doi":"10.21203/rs.3.rs-6852089/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T18:42:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T20:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152979519908661408789379563721763988993","date":"2025-09-23T08:29:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T10:12:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256472492748868786727340020757680144889","date":"2025-08-26T14:56:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-26T14:29:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T06:52:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T05:38:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Urban Sustainability","date":"2025-06-09T07:52:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"568c0579-c202-4821-bcec-21a092b6e4c8","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54081468,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":54081469,"name":"Social science/Anthropology"},{"id":54081470,"name":"Social science/Economics"},{"id":54081471,"name":"Social science/Environmental studies"},{"id":54081472,"name":"Social science/Social policy"}],"tags":[],"updatedAt":"2025-12-29T15:59:37+00:00","versionOfRecord":{"articleIdentity":"rs-6852089","link":"https://doi.org/10.1038/s42949-025-00316-7","journal":{"identity":"npj-urban-sustainability","isVorOnly":false,"title":"npj Urban Sustainability"},"publishedOn":"2025-12-26 15:57:13","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-09-02 16:10:00","video":"","vorDoi":"10.1038/s42949-025-00316-7","vorDoiUrl":"https://doi.org/10.1038/s42949-025-00316-7","workflowStages":[]},"version":"v1","identity":"rs-6852089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6852089","identity":"rs-6852089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}