Introduction

The 2030 Sustainable Development Agenda and Sustainable Development Goal 10 prioritise addressing regional development imbalances (United Nations Department of Economic and Social Affairs [UN DESA], 2015; United Nations Development Programme, 2019). Global coastal and interior areas, which represent developed and underdeveloped regions, respectively, are key focal points for sustainable development research. Empirically assessing the long-term developmental trends and balance between these regions is fundamental for shaping future strategies for global regional development and can be achieved by examining the dynamic distributions of economic and population factors among regions and per capita GDP.

Between 2000 and 2018, the proportion of economic activity within coastal areas relative to the global total decreased from 67.25% to 63.02% (Jin et al., 2023) while the global population’s share declined from 49.71% to 48.97%. The ratio of per capita GDP between coastal and interior areas dropped from 2.08 to 1.78 (Jin et al., 2024), reflecting a narrowing of 0.3 over the past two decades and indicating a trend toward “coastal remoteness” (Supplementary Fig. S1; Supplementary Tables S1 and S2). However, coastal and interior areas’ development faces numerous uncertainties with global population ageing emerging as a particularly important factor (Bloom et al., 2015). In previous assessments of regional development and balance, GDP allocations for pensions were not deducted from total regional GDP, leading to a potential overestimation of regional balance as measured by total economic output (or per capita GDP) (United Nations, 2023; OECD, 2021). Concurrently, between 2000 and 2018, the proportion of the population aged over 65 years increased from 7.81% to 10.01% in global coastal areas and from 6.39% to 7.83% in interior areas (WorldPop, 2019). Per international standards, an area has an ageing population when the proportion of those aged 60 or 65 years and over exceeds 10% or 7% of the total population, respectively. Therefore, both coastal and interior areas have entered an ageing phase. Continued growth in the global ageing population will result in pensions occupying a larger share of global regional economic output. The shift in population structure will also precipitate ongoing changes in global workforce size, inevitably affecting the future development trajectory of these two types of regions (Faruqee and Mühleisen, 2003; Lindh and Malmberg, 1999; MacKellar et al., 2004; UN DESA, 2019).

Accordingly, we primarily address the following three core issues: First, in the global ageing context, we investigate whether the two major regional blocs can sustain the balanced development trend observed over the past 20 years until 2060. We examine whether a time point exists at which these regions could achieve absolute balance. If it does, we evaluate the duration required for the economic sizes or per capita GDPs of the two blocs to converge. Conversely, if no intersection is predicted, the future characteristics of the two blocs are analysed. Second, we calculate the scale of the older-adult population, personal pension standards, and pension sizes within the two regions over the medium to long term. We subsequently estimate the actual GDP of these blocs. This analysis aims to reveal how the deepening degree of ageing and corresponding expansion in pension sizes impact long-term development and trends toward a balance between regions. Third, we conduct policy simulations involving delayed pension disbursement and extended retirement ages. When extending the retirement age from 60−65 years and from 65−70 years, and considering a delayed disbursement of five years, we calculate the standards, scales, and regional proportions of pensions for different retirement ages. Additionally, we assess delayed retirement’s marginal contributions to long-term regional development in terms of GDP and labour force size, addressing global ageing’s impact on long-term regional development and equilibrium. We identify potential optimal paths and development goals for the two regions within the context of ageing. This analysis aligns with multiple international concerns and global initiatives, such as the United Nations’ 2030 Agenda for Sustainable Development (United Nations, 2015) and the World Population Prospects (United Nations, 2019), by examining how population ageing influences regional economic balance, rational utilization of terrestrial and marine resources, environmental protection, and the sustainability of economic growth. In doing so, it builds on existing studies of demographic shifts and public policy (Bloom et al., 2010c; Lee and Mason, 2011; OECD, 2021) to assess potential paths and policy interventions for balanced regional development.

To carry out this analysis, an understanding of the potential spatial distributions of future populations and economies under various conditions is crucial. The shared socioeconomic pathways (SSPs) approach offers a suitable global framework for addressing the uncertainties of regional development. SSPs were initially developed by the integrated assessment modeling and climate research communities (Kriegler et al., 2012; O’Neill et al., 2014; Schweizer and O’Neill, 2014; O’Neill et al., 2016; O’Neill et al., 2016) to provide a consistent framework for integrated scenario analysis, including those used by the Intergovernmental Panel on Climate Change (IPCC). These pathways describe alternative trajectories of global societal development in terms of social, economic, and environmental trends under varying challenges to mitigation and adaptation. The SSPs framework was later incorporated into the Scenario Model Intercomparison Project (ScenarioMIP) under CMIP6, and formed the basis for the socioeconomic scenarios used in the IPCC Sixth Assessment Report (AR6). Although the Shared Socioeconomic Pathways (SSPs) were developed by the international climate change research community, they provide a diverse set of scenarios for social, economic, and environmental development that can be used to explore potential synergies and trade-offs with the Sustainable Development Goals (SDGs). This makes them a valuable tool for assessing long-term trends in both coastal and interior areas (O’Neill et al., 2016). Thus far, demographic and economic studies concerning SSPs have predominantly been conducted on global and national scales (Abel et al., 2016; KC and Lutz, 2014, 2017; KC et al., 2017) without considering the population and economic dynamics specifically within coastal and interior areas.

This study expands upon the global framework of SSPs to include economic and population research for coastal and interior areas, assessing their development gaps from 2030−2060. Our assessment considers the ageing population’s age structure to ascertain the equitability of future global coastal and interior area development. We also identify potential optimal development pathways, aiming to provide data support and policy guidance for future global coastal and interior area development strategies. Notably, in the subsequent analysis and explanation, the terms ‘coastal area’ and ‘interior area’ are respectively used to refer to global coastal and global interior areas.

Results

Scale of older-adult population and population ageing in global coastal and interior areas under SSPs

We delineate the older-adult populations at ages of 60, 65, and 70 years across global coastal and interior areas. Our analysis indicates that from SSP1 to SSP5, the older-adult population in coastal areas consistently exceeds that in interior areas, with both regions exhibiting a general upward trend over time (Fig. 1a). Notably, increasing the baseline older-adult age by five years leads to a reduction of approximately 200 million in the overall older-adult population. For example, under SSP1, the aggregate numbers of individuals aged 60, 65, and 70 years in coastal areas worldwide increase from 780 million, 570 million, and 380 million in 2030 to 1.51 billion, 1.23 billion, and 950 million by 2060, respectively. Similarly, for interior areas, the totals for the same age groups increase from 630 million, 440 million, and 290 million in 2030 to 1.25 billion, 1.0 billion, and 750 million in 2060, respectively. The older-adult population in the other SSPs also exhibits an increasing trend (see Fig. 1a, b; Supplementary Tables S3 and S4 provide detailed statistics). This substantial growth in the number of older adults accelerates the ageing process within the corresponding regions.

Fig. 1: Global comparison of elderly population size and ageing rates (Aged 60, 65, and 70+) between coastal and interior areas.
Fig. 1: Global comparison of elderly population size and ageing rates (Aged 60, 65, and 70+) between coastal and interior areas.
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a Comparison of the size of the older-adult population in the global coastal and interior areas under shared socioeconomic pathways (SSPs) in 2030 and 2060. b Changes in the size of the older-adult population under different baseline ages. c Comparison of aging levels of the two major regions in 2030 and 2060. d Global aging level.

When adopting ages of 60 and 65 years as benchmarks for entering the ageing phase, both coastal and interior areas are forecasted to enter this stage by 2030, with the process intensifying over time. Under SSP1 and SSP5, the degree of population ageing is particularly apparent (Fig. 1c). By 2030, when using 60 years as the age threshold, the extent of ageing within the coastal population in SSP1 to SSP5 will range from 18% to 20%, whereas that in interior areas will range from 8%−16%. When using 65 years as the age threshold, coastal ageing rates will range from 13%−14% and interior rates from 7%−16%. By 2060, for the age of 60 years, coastal ageing levels will range from 28%−39% and interior levels from 20%−33%. For the age of 65 years, the coastal ageing population will range from 22%−32% and the interior population from 13% to 27% (Supplementary Table S5 provides detailed data). These values considerably exceed the projections in the UN’s 2022 World Population Prospects that the global population aged ≥65 years would increase from 10% in 2022 to 16% in 2050 (Fig. 1d). Under SSP2, global ageing is expected to reach 17% by 2040, indicating that this process will occur at least a decade earlier than initially forecasted.

Pension standards and scales in global coastal and interior areas under SSPs

Pension standards in global coastal and interior areas exhibit a notable upward trend across SSPs with the most pronounced increases under SSP5 and SSP1 (Fig. 2a). The increase in pension standards becomes more pronounced as retirement age is extended. Specifically, increasing the pension distribution age from 65−70 years precipitates a greater rise in pension standards compared with increasing the distribution age from 60−65 years (Fig. 2b). This increases pension remuneration and improves older adults’ societal well-being (Supplementary Table S6). Additionally, pension standards in interior areas surpass those in coastal areas overall, with future increments in interior areas (the highest increment is approximately US $ 60,000 under SSP5) being substantially higher than those in coastal areas (the highest increment is approximately US$50,000 under SSP5).

Fig. 2: Comparison of pension standards, pension scales, and pension-to-GDP ratios between global coastal and interior areas (2030-2060).
Fig. 2: Comparison of pension standards, pension scales, and pension-to-GDP ratios between global coastal and interior areas (2030-2060).
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a Comparison between pension standards of global coastal and interior areas in 2030 and 2060 under shared socioeconomic pathways (SSPs). b Variation in pension standards from 2030−2060. c Comparison of pension scales between 2030 and 2060. d Changes in pension scales from 2030 to 2060. e Proportion of pension scales to GDP.

With the increasing older-adult population and general population ageing, rising pension standards will lead to a substantial increase in pension scales (Supplementary Tables S7 and S8). Across SSP1 to SSP5, coastal area pension scales are generally higher than those in interior areas (Fig. 2c). However, under all pathways, both coastal and interior areas exhibit a decreasing pension scale trend as the pension distribution age increases (Fig. 2d). Assuming a standard retirement age of 60 years and excluding SSP3 and SSP4, under SSP2, global pension scales for both coastal and interior areas increase from US $ 19 trillion in 2030 to US $ 46 trillion in 2060, raising their share of global GDP from 23% to 29%. Furthermore, delayed retirement age’s impact on pension scales and their share of global GDP is not substantial in this pathway (Supplementary Tables S9 and S10). However, under SSP1, global pension scales for coastal and interior areas are estimated to increase from approximately US $ 23 trillion in 2030 to $ 85 trillion in 2060, with the global GDP share growing from 26%−43%. If the retirement age is delayed to 65 years, these values decrease to approximately US $ 20 trillion (23%) in 2030 and $ 83 trillion (42%) in 2060. If the retirement age is further extended to 70 years, these values decrease to approximately US $ 19 trillion (21%) in 2030 and US $ 79 trillion (40%) in 2060. SSP5 exhibits similar characteristics to SSP1 (Fig. 2e).

Additionally, across all pathways, delaying retirement age by five years reduces global pension scales equivalent to the 2022 GDP of the United Kingdom (approximately US $ 3 trillion), accounting for approximately one-fifth of the total 2022 GDP of the European Union. Furthermore, under SSP1 and SSP5, a five-year delay in retirement age leads to a reduction in global pension scales equivalent to the 2022 GDP of Germany (approximately US $ 4 trillion), constituting approximately one-fourth of the total 2022 GDP of the European Union.

Spatial evolution and balanced trends of economic and population factors in global coastal and interior areas

Figure 3 presents the change trends of economic and population factors in global coastal and interior areas from 2030 to 2060 under the SSPs. Economic factors generally shift from coastal to interior areas across SSP1 to SSP5, representing a spatial evolution known as ‘coastal remoteness’. However, the magnitude is modest with coastal areas still concentrating approximately 60% of the total global GDP, maintaining the ‘core-periphery’ structure. After considering the impact of ageing, the coastal remoteness trend for economic factors slows considerably. Regarding population, under all SSPs, the proportions between the two major regional blocs remain relatively balanced, although, for certain pathways, the population in global interior areas exceeds that in global coastal areas. In SSP1 and SSP5, population proportions in global coastal and interior areas remain relatively stable, exhibiting weak ‘coastal proximity’ spatial evolution. Conversely, in SSP2, SSP3, and SSP4, the population proportion decreases in global coastal areas, with greater growth in global interior areas, indicating a population transfer from coastal to interior areas, demonstrating ‘coastal remoteness’ spatial evolution.

Fig. 3: Proportions of population and economic factors in global coastal and interior areas with (a) and without (b) aging impact.
Fig. 3: Proportions of population and economic factors in global coastal and interior areas with (a) and without (b) aging impact.
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Proportions of population and economic factors in global coastal and interior areas with and without aging impact.

These results reflect a global trend wherein despite the impact of population ageing, economic and population factors exhibit relatively stable development patterns across the two major regions. However, for certain pathways, the occurrence of either ‘coastal remoteness’ or ‘coastal proximity’ for economic and population factors reflects differing development policies and the redistribution of economic activities on a global scale, as well as the decoupling of economic and population growth factors.

The assessment of future development trends of global coastal and interior areas indicates an increasing burden for pension scales due to population ageing, thus lowering actual GDP, which exacerbates the development gap between these two major regional blocs. Accordingly, we utilised Eqs. (1)–(14) to calculate the actual GDPs of global coastal and interior areas under different SSPs. This calculation involved deducting pension expenditures to determine the actual GDPs of both areas.

Table 1 reveals that across all SSPs, the magnitude of the change in the coastal-interior development gap is generally greater when ageing is not considered. The global ageing process substantially slows the narrowing of the development gap, particularly in SSP1 and SSP5, where the gap narrows more when ageing is not considered. Despite the effects of population ageing and pension expenditures, SSP1 and SSP5 still promote more balanced development between the two major regional blocs, though the regional development gap ratio remains around 1.6. Under SSP1, the regional development gap ratio decreases from 1.65 in 2030 to 1.63 in 2060. Under SSP5, it decreases from 1.63 in 2030 to 1.60 in 2060. Conversely, under SSP2, the development gap ratio increases from 1.70 in 2030 to 1.73 in 2060, and under SSP3 or SSP4, it increases to 1.82 by 2060.

Table 1 Development gaps in global coastal and interior areas.
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These results suggest that when considering population ageing and pension expenditure, the balanced development of global coastal and interior areas only occurs under SSP1 and SSP5. However, in both scenarios, the development gap’s reduction from 2030−2060 is only 0.03, which is much lower than the 0.3 reduction observed over the past 20 years (from 2000−2018, the development gap ratio decreased from 2.08 to 1.78). In the long term, the development gap between global coastal and interior areas is expected to maintain a relatively stable contraction range. This suggests a need to focus on the future development of these regional blocs without excessively pursuing absolute balance. Instead, regional development objectives should aim to maximise total economic output within each region while ensuring that the development gap remains within a reasonable contraction range.

Impact of extended retirement age on the economy, labor force size, and regional balance in global coastal and interior areas

To maintain a consistent reduction in developmental disparities between global coastal and interior areas and enhance overall economic output, we extended the pension payment age from 60 to 65 and 70 years. We aimed to identify variations in total economic output, workforce size changes, and the degree of impact on the regional balance between regions under different scenarios.

As shown in Supplementary Table S11, delaying retirement age expands total GDP within global coastal and interior areas across all pathways. Under SSP1 and SSP5, the cumulative increase in GDP is maximised (Table 2). Under SSP1, if the retirement age is increased from 60−65 years starting in 2030, the total cumulative GDP from 2030−2060 in coastal and interior regions will increase by US$73,018.0 billion (a cumulative marginal contribution of 5.0% to the regional economy) and US$76,997.4 billion (a cumulative marginal contribution of 8.6% to the regional economy), respectively. Under SSP5, total cumulative GDP increases of US$81,392.3 billion (a cumulative marginal contribution of 4.5% to the regional economy) and US$95,549.2 billion (a cumulative marginal contribution of 8.5% to the regional economy), respectively, are expected. By 2060, the cumulative economic increase in coastal and interior areas will account for 14% of total global GDP from 2030−2060. If the retirement age continues to be delayed to 70 years, total cumulative GDP under SSP1 in the coastal and interior areas will grow by an additional US$63,745.3 billion (a cumulative marginal contribution of 4.2% to the regional economy) and US$74,770.9 billion (a cumulative marginal contribution of 7.8% to the regional economy), respectively. Under SSP5, the total cumulative GDP increases by an additional US$71,555.4 billion (a cumulative marginal contribution of 3.8% to the regional economy) and US$93,373.4 billion (a cumulative marginal contribution of 7.7% to the regional economy) in coastal and interior areas, respectively. By 2060, the cumulative economic increase in coastal and interior areas will account for 12% of total global GDP from 2030−2060. Table 2 shows that under SSP1 and SSP5, the marginal benefits of delaying retirement age on the total economic output of interior areas exceed those of coastal areas. Consequently, interior areas with a smaller older-adult population experience relatively more ‘population dividends’. By 2060, compared with coastal areas, interior areas demonstrate greater potential and benefit more from the positive effects of an aging population.

Table 2 Marginal contributions to real GDP for global coastal and interior areas from 2030 to 2060 at different retirement ages.
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Additionally, by extending the retirement age, individuals who would otherwise require government pension expenditures can remain active in the workforce, thereby contributing to an expansion of the global labor force. Under SSP1, starting in 2030, raising the retirement age from 60−65 years results in accumulated increases in the total labour force in coastal and interior areas of 1.02 and 0.9 billion, respectively. Similarly, under SSP5, the cumulative total labour forces within these regions increase by 1.03 and 0.89 billion, respectively, from 2030−2060, adding approximately 1.9 billion individuals to the global labour market. When retirement age is further extended to 70 years, under SSP1, the cumulative total labour forces of coastal and interior areas increase by an additional 0.94 and 0.8 billion, respectively. Similarly, under SSP5, the cumulative total labour forces within the regions rise by an additional 0.89 and 0.8 billion, respectively, from 2030−2060, contributing an additional 1.7 billion individuals to the global labour market (Table 3).

Table 3 Marginal contributions of extending retirement age to labour forces in global coastal and interior areas.
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Simultaneously, extending the retirement age effectively improves regional development balance (Supplementary Table S12). By 2060, under pension expenditure standards and retirement ages set at 65 and 70 years, the relative development gap between global coastal and interior areas remains slightly narrowed in both SSP1 and SSP5, suggesting that the two major regions will continue trending toward balanced development from 2030−2060. However, adherence to the existing development pathway of SSP2 or extreme development pathways of SSP3 and SSP4 would precipitate varying degrees of widening of the development gap.

Excluding pathways that exacerbate regional development disparities (SSP2, SSP3, SSP4), under SSP1 and SSP5, each five-year extension of pension payout age or retirement age further facilitates the narrowing of development gaps between global coastal and interior areas. Taking SSP1 as an example, when retirement age is set at 60 years, the development gap ratios between the two regions are 1.649, 1.604, 1.632, and 1.626 in 2030, 2040, 2050, and 2060, respectively. However, if retirement age is postponed to 6 years, the development gap ratios between the two major regions are 1.642, 1.596, 1.593, and 1.624. Further delaying retirement to 70 years results in development gap ratios of 1.636, 1.585, 1.570, and 1.607. The same trend occurs for the regional development pathway under SSP5. Dynamic adjustments to the age composition of the labour force facilitate the balanced development of global coastal and interior areas. Consequently, in the context of a steadily ageing population, prioritising both the maximisation of economic output and labour force size as well as balanced regional development in global coastal and interior areas, SSP1 emerges as the optimal pathway.

Discussion

Investigating how certain characteristics affect balanced development in coastal and interior areas is key to achieving global sustainable development. Jin et al. (2023) identified the ‘coastal remoteness’ spatial evolution pattern, highlighting the shift of economic factors from global coastal to interior areas from 2000 to 2018. Building on this key finding, we identified the spatial evolution trends of population factors in global coastal and interior areas during the same timeframe. By analysing the gap in per capita GDP, we identified a pattern of gradually narrowing development disparities and increasing balance between the two regions. Recent studies also confirm that demographic shifts, along with patterns of industrial relocation from coastal megacities towards inland regions, are significantly reshaping the economic spatial structure and regional disparities (Nathan and Overman, 2020; Nijman and Wei, 2020). Through an analysis of per capita GDP gaps, we identify a gradual convergence in regional developmental disparities between coastal and interior areas, which aligns closely with recent research on convergence trends in spatial economics (Iammarino et al., 2019). Subsequently, we explored the future development trajectories of global coastal and interior areas. Notably, we incorporated factors of population age structure into our dynamic equilibrium assessments. Furthermore, some studies suggest that variations in population age structure may lead to potential overestimations of regional economic development in SSPs-based projections when slower labor force growth or reduced productivity among older cohorts is insufficiently accounted for (KC and Lutz, 2017; Lutz et al., 2014). This does not imply that IIASA’s scenarios are universally overstated; rather, in specific regions or countries where demographic shifts impose greater constraints on labor markets or productivity, actual economic growth may fall short of earlier model projections. Moreover, our study shows that under SSP1 and SSP5, pension expenditures exceed 40% of global GDP, which in turn can affect national economic growth expectations.

Furthermore, we aimed to determine whether global coastal and interior areas can achieve a more optimal state under certain developmental conditions in the future. Specifically, we explored which development pathways could further reduce development disparities between the two major regions and effectively enhance their respective economic aggregates. Because SSPs delineate different trajectories for future socioeconomic development, they can better illustrate various scenarios for the future development of global coastal and interior areas. Leveraging these SSPs, we identified the development prospects of both regions, extending them to comprehensive assessments of future development. Such scenario expansion research provides an essential foundation for the broader application of SSPs and vital support for policymaking regarding both coastal and interior areas.

Uneven development of regional economies is a persistent and long-term process. We found that under all projected development pathways, coastal areas are expected to account for approximately 60% of global GDP by 2060. Existing studies have found that regional economic development is inherently constrained by geographical endowments and agglomeration economies (Balland et al., 2019). Coastal areas have long maintained economic dominance during the industrial and post-industrial stages due to the advantages of globalization and favorable trade conditions. However, recent research also confirms that the high cost of living, congestion, and environmental challenges in coastal megacities are increasingly driving the relocation of economic and population factors toward interior areas, gradually shaping a new spatial equilibrium pattern (Nathan and Overman, 2020; Rodríguez-Pose and Storper, 2020; Jin et al., 2023, 2024). The resource endowment theory emphasises that regional economic development is inherently constrained by available resources. For example, coastal areas benefit from convenient shipping conditions and became economic hubs during the late industrialisation and post-industrial stages. By contrast, interior areas continue to face disadvantages. Therefore, the future balance between the development of global coastal and interior areas depends on addressing these disparities. However, given the market congestion effect in global coastal areas, rising consumer and producer price indices, and cumulative causation mechanisms, there is a trend for mobile factors (e.g. the economy and population) to move gradually from coastal to interior areas (Jin et al., 2023, 2024; Fujita and Thisse, 2013; Myrdal, 1957; Krugman, 1991). Over the long term, this indicates that regional development policies in the global economy do not simply expand the industrial share of high-agglomeration regions (i.e., coastal areas) or diminish it in low-agglomeration regions (i.e., interior areas), a scenario that would otherwise widen the development gap between these two major blocs (Henderson et al., 2018; Jin et al., 2024). Instead, by facilitating the flow of key factors between coastal and interior areas, such policies continuously enhance development balance. This dynamic thus constitutes the core mechanism underlying changes in the global development gap between coastal and interior areas.

However, pursuing absolute balance between global coastal and interior areas is unrealistic. We aim to implement policy measures to prevent developmental gaps from widening, to maintain these gaps within a reasonable or stable contraction range, and to enhance regional economic development capacity. Existing studies suggest that from the early to mid-21st century, the relative developmental gaps between global coastal and interior areas have not uniformly contracted but instead exhibited cyclical or ‘pendulum-like’ trends (Puga, 1999; Henderson et al., 2018). Our findings indicate that between 2000 and 2020, economic globalization facilitated a comparatively rapid shift from imbalance to balance, with a development gap ratio of approximately 0.3. However, from 2030 to 2060, particularly given the expanding impact of population ageing, this gap is projected to contract at a much slower rate, dropping to around 0.03 (Lee and Mason, 2011). This also confirms the regional economic growth theory based on neoclassical economic growth theory, which holds that regions will eventually tend toward long-term stability and balance. Therefore, formulating policies promoting the development of both coastal and interior areas is necessary. Efforts should focus on achieving further balance within a stable range of shrinking gaps and expanding the economic scale of both regions.

Additionally, population age structure directly impacts the variation in developmental disparities between coastal and interior regions. According to the United Nations, over the next 50 to 100 years, the world will experience substantial ageing. An ageing population will mean an older workforce and key changes in social structures, along with disruptions in economic structures and operations (Bloom et al., 2010a; Bloom et al., 2010b). These shifts will affect consumption/demand patterns in both coastal and interior areas, with increased spending on older-adult care being the most direct impact on regional economies. Existing studies indicate that labour-force shrinkage, rising pension expenditures, and shifts in consumption patterns pose significant challenges to regional economic development (OECD, 2023; Bodnár and Nerlich, 2022; Bloom and Eggleston, 2014). The 2023 World Social Report underscores the imperative for governments to reassess their social security systems, including pensions (United Nations, 2023), to ensure older-adult welfare while expanding economic productivity. In both developed and developing economies, pensions constitute a considerable fraction of overall economic output. As the older-adult population grows, both public and private sectors will face escalating pension obligations. Consequently, a progressively larger share of national income will be directed to older-adult support. Therefore, pension scale is a crucial factor to consider in regional economic development as it influences actual economic development levels and the relative gaps between regions. Furthermore, the influence of population age structure has led to a degree of overestimation of regional economic development under the SSPs by the International Institute for Applied Systems Analysis (IIASA).

Furthermore, the global ageing phenomenon does not solely negatively impact regional development and balance between coastal and interior areas. With their relatively mature skills, experience, and resources, older adults constitute a key asset to socioeconomic development, and their productivity should be encouraged. Indeed, there has been an international shift in perspectives on population ageing. Laslett (1991) emphasised that many retired older adults can still actively pursue their goals, thereby driving the transformation of national social structures. Butler, Gleason (1985) concept of ‘productive ageing’ highlights older adults’ social engagement, acknowledging their inherent productivity despite health and social challenges. The ‘active ageing’ concept extends older adults’ social involvement beyond the economic domain to various aspects of society. Therefore, to support balanced regional development and overall economic output, extending the retirement age within certain regions may be beneficial. Future research aimed at adjusting workforce age structure to foster equilibrium between global coastal and interior areas and strengthen regional economic development can gradually shift the assessment of population ageing from negative to positive. This approach addresses the decision-making contradiction between a burgeoning older-adult population and the balanced development faced by the two regions.

This study delved deeper into expenditures on older-adult care, revealing a contracting developmental gap between global coastal and interior areas only under SSP1 and SSP5. Conversely, if SSP2 persists until 2050, then the developmental gap between these two regions will widen. While SSP5 represents a pathway of rapid economic expansion driven by technological innovation and fossil fuels, it raises serious concerns about climate and environmental sustainability. The scenario envisions a world of rapid economic expansion, particularly through technological development and increased industrial activity. However, the environmental costs of such growth, primarily driven by fossil fuel consumption, raise concerns about long-term sustainability (Kriegler et al., 2017). The most recent IPCC assessment indicates that, without large-scale carbon removal, keeping global warming below 1.5 °C under the SSP5 scenario would be infeasible (IPCC, 2022). Kriegler et al. (2017) further show that, if emissions remain uncontrolled, SSP5 could add roughly 1,000 Gt CO₂ this century—about twice the remaining carbon budget compatible with limiting warming to 2 °C. Under SSP5, coastal areas benefit from accelerated globalization, but these benefits come with the risk of exacerbating climate change impacts. This scenario could lead to increased inequality both within and between regions, particularly if the environmental damage from fossil fuel reliance is not adequately addressed. In light of these concerns, the potential for achieving equitable global development under SSP5 is highly contingent upon the implementation of effective climate mitigation policies. Without such measures, the development benefits in coastal areas might not be sustainable or could result in significant environmental degradation. Simultaneously, SSP5 conflicts with several Sustainable Development Goals (SDGs 7, 13 and 3). Heavy fossil-fuel use slows the spread of clean energy, increases climate-related health risks for older adults (World Health Organization WHO (2023)), and raises inter-generational fairness concerns. Therefore, although SSP5 represents an attractive option for economic growth in coastal areas, the intense use of fossil fuels and the lack of significant global climate cooperation present considerable barriers to achieving sustainable, equitable global development. The pursuit of this pathway requires a delicate balance between rapid economic expansion and the need for climate change mitigation. Notably, SSP3 and SSP4 scenarios, characterized by protectionism and weaker global cooperation, can further widen the divide between coastal and interior areas by reinforcing coastal technological monopolies and limiting the diffusion of innovations inland. As Krugman (1991) argues, core–periphery dynamics arise when regions with existing advantages in infrastructure, knowledge, and trade networks consolidate their lead, making it harder for peripheral or interior areas to catch up. Under more protectionist conditions, trade barriers and restricted capital flows constrain inland innovation and growth, intensifying the concentration of economic and technological activities in coastal hubs. Therefore, truly achieving balanced development by the mid-century point will necessitate adopting SSP1. Simultaneously, with each additional five-year postponement of retirement age, the relative disparity between global coastal and interior areas further decreases, leading to a noticeable increase in regional economic aggregates, particularly under SSP1.

Although SSP5 supports technological progress and economic expansion, its scenario design is centred on fossil-fuel-intensive growth, with dependence on energy and emissions at the upper-bound range and corresponding to a very high warming endpoint (SSP5-8.5), which will exacerbate coastal climate risks such as sea-level rise, extreme heat, and ecological degradation (Kriegler et al., 2017; Riahi et al., 2017). In the absence of robust mitigation and coordinated governance, this pathway is unsustainable for many coastal areas and ageing populations: on the one hand, the exposure and vulnerability of coastal and urban systems to sea-level rise and extreme events increase, and adaptation faces limits and unavoidable residual risk; on the other hand, heat-exposure-induced declines in labour productivity and health burdens will have a more pronounced impact on older workers(Cissé et al., 2022). SSP5-8.5 is explicitly designated in IPCC AR6 as an upper-bound reference scenario for very high emissions/very high warming, used for risk assessment rather than as a policy target or a “conventional scenario.” Existing studies show that, compared with stand-alone “protection” strategies, combining engineered protection with migration/retreat (managed retreat) and ecosystem-based measures can reduce long-term macroeconomic costs across multiple regional scales, but under very high-emissions pathways, residual risk remains substantial and requires stronger mitigation and an orderly transition as complements (Bachner et al., 2022; Mach and Siders, 2021; Hinkel et al., 2014). For densely populated coastal areas, climate-driven forced-migration risks, productivity losses due to heat exposure, and the distributional impacts on older workers all raise important ethical and intergenerational-equity issues, which have been systematically discussed in the World Bank’s Groundswell reports and in the health–society chapters of IPCC AR6 (Rigaud et al., (2018)). At this level, SSP1 should also be prioritised.

Concerning this study’s limitations, global coastal and interior areas’ future development patterns are influenced by multiple factors, including changes in industrial structures, capital flows, resource endowments, and policy frameworks; however, this study primarily focused on the context of population ageing considering the influence of pension policies and delayed retirement age to evaluate the equilibrium dynamics of global coastal and interior areas and identify optimal regional development pathways. Future research should integrate additional factors and enhance scenario development using multiple models to refine the analysis and path selection of coastal-interior development trends at various regional scales. Simultaneously, the current scenario-based simulation approach, rooted in demographic and economic projections rather than statistical inference from sample data, does not involve conventional statistical hypothesis testing or associated effect-size metrics. Future research could incorporate micro-level data or employ empirical methods such as panel regressions, and report effect size indicators and power analyses, to validate the robustness and statistical significance of policy impacts identified by scenario analyses.

Conclusion

Our major findings can be summarised as follows:

First, between 2030 and 2060, global ageing is likely to exceed the United Nations’ projections. By the mid-21st century, ageing populations will expand in both coastal and interior areas under all pathways, particularly under SSP1 and SSP5. Additionally, the ageing population in coastal areas is expected to exceed that in interior areas. Meanwhile, our findings show that the marginal benefit of delaying retirement is greater in interior areas—8.6%, compared to only 4.5% in coastal areas—indicating that appropriate delayed retirement policies can help achieve balanced global growth.

Second, excluding the extreme SSP3 and SSP4 scenarios, by 2060, pension funds under SSP2 are projected to account for approximately 30% of global GDP and exceed 40% of global GDP under SSP1 and SSP5. This will considerably impact regional long-term development and balance. Furthermore, as the pension distribution age is extended, pension fund size will gradually decrease. Delaying pension age by five years across all scenarios yields a reduction in global pension fund size equivalent to the United Kingdom’s 2022 GDP (US $ 3 trillion). Under SSP1 and SSP5, this reduction is equivalent to Germany’s 2022 GDP (approximately US $ 4 trillion). Regardless, postponing pension distribution age is expected to elevate individual pension standards, thereby enhancing older-adult social welfare.

Third, the global ageing process has substantially impeded the narrowing of developmental gaps between coastal and interior areas. Between 2030 and 2060, SSP2, SSP3, and SSP4 are projected to widen these gaps. Only SSP1 and SSP5 will sustain a long-term reduction in gaps, with the gap ratio contracting to approximately 1.6, essentially reaching a balanced point. However, the maximum narrowing extent is 0.03—merely one-tenth of the reduction achieved in the past two decades. Absolute balance appears elusive in the future. While prioritising the contraction of development gaps, maximising the total economic scale within each region may emerge as the optimal objective for global regional development. Influenced by population ageing, during the study period, coastal areas will continue concentrating approximately 60% of global GDP, maintaining a core-periphery structure with interior areas over a prolonged period. Additionally, under SSP1 to SSP4, economic factors exhibit a spatial trend of coastal remoteness, whereas SSP5 shows a tendency toward coastal proximity. Similarly, population factors exhibit a coastal proximity pattern under SSP1 and SSP5. However, changes in economic and population factors are not markedly pronounced across the two regions, with a discernible reverse trend in the spatial evolution of these factors.

Fourth, delaying retirement positively influences long-term global economic growth, global labour market size, and balanced coastal-interior development. Postponing the retirement age by five years and implementing SSP1 and SSP5 may promote economic output in the short term but entails significant climate risks, requiring caution and effective mitigation policies. If retirement delay policies begin in 2030, with retirement postponed to 65 years, under SSP1 and SSP5, combined global economic output could increase by approximately 14% with 1.9 billion people added to the labour force between 2030 and 2060. Development gap ratios in global coastal and interior areas would also decrease from 1.626 to 1.624 and from 1.600 to 1.594, respectively. Postponing retirement age to 70 years could add an additional 12% to cumulative global economic output and approximately 1.7 billion workers, with development gap ratios shrinking further to 1.607 and 1.574, respectively.

Considering that sustainable development remains the core framework for achieving long-term equilibrium between the two major regions, and influenced by the global trend of population ageing, future global coastal-interior areas should select the SSP1 pathway, appropriately extend the retirement age, and emphasise the labour productivity of individuals aged between 60 and 70 years. Simultaneously, avoiding SSP3 and SSP4 is imperative, as these will trigger conflicts in the development of global coastal and interior areas and widen their development gap. Meanwhile, it is important to emphasize that although SSP5 supports rapid technological and economic advancement, its reliance on fossil fuels may exacerbate environmental degradation. This pathway’s heavy dependence on fossil fuels underscores the necessity of stringent mitigation strategies to avert long-term negative impacts on global coastal-interior areas.

Experimental Procedures

Resource availability

Materials availability

This study did not generate new unique materials.

Data and code availability

The global GDP forecasting data under various SSPs utilised in this study were provided by IIASA (https://tntcat.iiasa.ac.at/SspDb/). Data concerning global population size were taken from globally gridded population projection data (https://figshare.com/s/9a94ae958d6a45684382). Population age structure data were derived from prediction data provided by IIASA’s World Population Program (IIASA-WIC POP) based on SSPs data (https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about#v2). National savings rate and basic map data were sourced from the World Bank (https://data.worldbank.org.cn/). Capital stock data were sourced from the ‘capital detail’ data in the Penn World Table (version 10.01) (https://www.rug.nl/ggdc/productivity/pwt/?lang=en).

Study area

This study categorised the world into two regions, namely coastal and interior areas. Specifically, coastal areas include areas within 100 km of the sea (i.e. ocean coast or ocean-navigable river), and interior areas include areas over 100 km from the sea (Supplementary Fig. S2). Subsequently, a comprehensive database incorporating GDP, population, savings, and area attributes for 172 countries was created. The supplementary information provides the detailed criteria and justifications regarding regional classifications.

Regional development scenario settings under SSPs

Considering the complex factors affecting development in coastal and interior areas, relying on historical data and patterns to predict future changes is challenging. To represent the myriad of evolutionary possibilities in these two regions and ultimately promote robust policymaking, additional research on coastal and interior areas is necessary. The SSPs framework integrates assumptions regarding both socioeconomic and climatic factors, with the latter providing necessary context for understanding potential development scenarios (O’Neill et al., 2014; Riahi et al., 2017). However, it is important to clarify that this study does not directly address the impacts of climate change, but rather uses the SSPs to explore potential socioeconomic futures, considering population dynamics, economic trends, and environmental challenges. This SSPs framework is grounded in extensive experimentation and discussion, having undergone various expert panel discussions to ensure internal consistency of the scenarios (Maier et al., 2016; Moss et al., 2010; Van Vuuren et al., 2012a, b). The framework incorporates six critical elements for establishing future socioeconomic scenarios: population, human development, economics and lifestyles, policies and institutions, technology, and environment and natural resources (Van Vuuren et al., 2014; O’Neill et al., 2016). Among the proposed five typical SSPs pathways, four (SSP1, SSP3, SSP4, SSP5) set different combinations of high or low mitigation and adaptation challenges, whereas in SSP2, both challenges are relatively moderate. In most cases, future socioeconomic development is expected to fall between two or more of these pathways. We redefined the five development pathways for global coastal and interior areas as follows.

  1. (1)

    SSP1 (Sustainability Pathway): SSP1 represents a human-centric avenue toward sustainability, often referred to as the ‘green pathway’ (Van Vuuren et al., 2017a, b). This scenario is characterised by global efforts to achieve sustainability while reducing resource intensity and fossil fuel reliance. Focus shifts from economic growth to human well-being. Due to early economic accumulation, per capita income levels grow rapidly. Population growth in major regional blocs is controlled at a slow pace, leading to a gradual equilibrium in development between coastal and interior areas.

  2. (2)

    SSP2 (Middle-of-the-Road Pathway): This pathway falls between SSP1 and SSP3 (Fricko et al., 2017). It maintains a developmental trajectory consistent with historical patterns from the past century, including in global coastal and interior areas. On average, spatial development gaps persist between coastal and interior areas. Historically, growth patterns in these areas are projected to continue at the same pace, facing moderate challenges in mitigation and adaptation, yet spatial developmental imbalances remain.

  3. (3)

    SSP3 (Regional Rivalry Pathway): The regional rivalry pathway departs from global cooperation, favouring regional competition (Fujimori et al., 2017). It envisions a world characterised by global fragmentation and isolation among regions, with national policies prioritising security and self-interest. Security-oriented economic de-globalisation leads to strengthened trade barriers and severely restricted international trade. Consequently, coastal areas lose their status as hubs of international trade coordination, while interior areas, due to poor management, lack distinct advantages to attract population and industry. Limited cooperation and communication between coastal and interior areas slow economic growth, creating a world where economic and population transitions stagnate, facing considerable adaptation challenges.

  4. (4)

    SSP4 (Inequality Pathway): This pathway foresees a world of uneven development, with rising imbalance and stratification both within and among nations (Calvin et al., 2017). It imagines a highly unequal world, with the development gap between coastal and interior areas widening further. Coastal economies boom, attracting more population and economic resources, while interior areas lag behind. Due to protectionism and insufficient global cooperation, SSP3 may exacerbate core–periphery imbalances by reinforcing trade barriers and intensifying regional disparities (Fujimori et al., 2017; Krugman, 1991). Conversely, interior populations face heavier economic burdens and are relatively disadvantaged regarding openness and opportunities.

  5. (5)

    SSP5 (Fossil-fuelled Development; Taking the Highway): SSP5 envisages a trajectory of rapid development, emphasising the pursuit of economic efficiency and swift global technology-driven economic growth (Kriegler et al., 2017). Under this scenario, accelerated globalisation and increasing reliance on fossil fuels spur extensive infrastructure investments—particularly in inland, resource-rich areas. This focus on fossil energy exploitation encourages the establishment of resource-based industries (e.g., coal, oil, or gas extraction and processing), along with energy-intensive manufacturing that can create new employment opportunities and capital inflows (Riahi et al., 2017). As interior areas develop their energy infrastructure, they attract additional industrial activities and improve transport networks, partially closing the traditional development gap with coastal areas. Although this intensified growth reduces regional economic disparities, it also raises concerns about environmental sustainability and climate change mitigation, underscoring the trade-offs inherent in a fossil fuel–centric pathway.

This study employs the SSP family as socioeconomic storylines coupled to demography and pension accounting. While we isolate pension-related fiscal flows and ageing effects quantitatively, we do not detach the analysis from the overarching SSP narratives (e.g., fossil-fuel intensity and mitigation challenges in SSP5). Therefore, all policy-relevant interpretations are normatively qualified: our economic gap metrics are conditional on the accounting scope and do not imply a normative ‘optimal pathway’ independent of climate-risk, equity and institutional feasibility considerations. Building on this, the SSPs framework ensures consistency among demographic shifts, economic trends, and environmental challenges. At the same time, the integration of core–periphery theory is essential: coastal areas often serve as high-density economic cores, while interior areas remain peripheral zones in economic geography (Krugman, 1991; Fujita et al., 2001). Whether disparities in trade policy, technology diffusion, and resource allocation exist will determine whether the imbalance between coastal and interior areas is reinforced or alleviated.

Data explanations

Global GDP forecasting data under various SSPs utilised in this study were provided by IIASA. Global GDP projections were expounded from primary drivers of economic growth under unified assumptions, elucidating the narrative trajectories of SSPs (Crespo Cuaresma, 2017; Dellink et al., 2017). Data on global population size were taken from globally gridded population projection data at a 1 km × 1 km resolution, which provide country-level population grid forecasts from 2000 to 2100 for each SSP (Merkens et al., 2016).

To calculate the population age structure for both coastal and interior areas up to 2060, we employed population age structure projection data from the SSPs dataset provided by IIASA-WIC POP. These data include information on age, gender, and total population size, with each SSP covering global population forecasting data for 193 countries in five-year intervals from 2015 to 2100 (Jun, 2005). The technical roadmap for this study is depicted in Fig. 4.

Fig. 4
Fig. 4
Full size image

Schematic representation of technical roadmap.

Estimation of actual per capita GDP in global coastal and interior areas

Globally, economic and population factors demonstrate a spatial evolution toward coastal remoteness, with these factors gradually shifting to interior areas. We utilised the SSPs framework to identify disparities in per capita GDP development between coastal and interior areas from 2030 to 2060. Initially, under different SSPs, the population (POP) and GDP in both regions can be defined as:

$$\left\{\begin{array}{c}{{GDP}}_{{global}}={{GDP}}_{{SEA}}+{{GDP}}_{{LAND}}\\ {{POP}}_{{global}}={{POP}}_{{SEA}}+{{POP}}_{{LAND}}\end{array}\right..$$
(1)

The per capita GDP for coastal and interior areas can be calculated using the following formula:

$$\left\{\begin{array}{c}{{PGDP}}_{{SEA}}^{{SSP}}=\frac{{{GDP}}_{{SEA}}^{{SSP}}}{{{POP}}_{{SEA}}^{{SSP}}}\\ {{PGDP}}_{{LAND}}^{{SSP}}=\frac{{{GDP}}_{{LAND}}^{{SSP}}}{{{POP}}_{{LAND}}^{{SSP}}}\end{array}\right..$$
(2)

The ratio of per capita GDP between global coastal and interior areas (\({D}_{{SEA}}^{{SSP}}\)) is determined as follows:

$${D}_{{SEA}}^{{SSP}}={{PGDP}}_{{SEA}}^{{SSP}}/{{PGDP}}_{{LAND}}^{{SSP}}.$$
(3)

In estimating the GDP for global coastal and interior areas, part of the calculation implicitly includes the economic share of pension expenditures, which do not actively contribute to the actual economic production process. As population ageing intensifies, the burden of pension expenditures is expected to increase, leading to an overestimation of current GDP levels within these regions. Consequently, the per capita GDP of these two major regions must be reevaluated, which will directly impact the assessment of the future development balance between the global coastal and interior areas. Therefore, we incorporated population ageing into the GDP calculation, starting from the age of 60 years (internationally, this age group is categorised as older adults, and globally, 60 years is often considered the retirement age with lower economic productivity, necessitating government-supported pensions). We calculated the scale of pension expenditures for the population aged 60 years and older to assess the actual developmental balance between coastal and interior areas after considering ageing factors. Furthermore, to analyse the impact of ageing populations and pension systems on regional development balance, we extended the retirement age in five-year increments, calculating pension expenditures for ages of 65 and 70 years. This allowed us to reassess actual GDP within regions and evaluate the future relative developmental balance between them. This exploration aimed to determine whether adjustments in retirement age might, under increasing population ageing, potentially narrow the development disparity between the two regions.

Initially, the older-adult population size and pension scale in global coastal and interior areas had to be calculated. The ‘older-adult population ratio’ refers to the number of older adults over the total population, which is typically expressed as a percentage. This value indicates the degree of population ageing. Aging levels with thresholds of 60, 65, and 70 years for 159 sample countries, denoted as (α60、α65、α70), were calculated using the following equation:

$$\left\{\begin{array}{c}{\alpha }_{60}=\frac{{{POP}}_{60}}{{POP}}\times 100 \% \\ {\alpha }_{65}=\frac{{{POP}}_{65}}{{POP}}\times 100 \% \\ {\alpha }_{70}=\frac{{{POP}}_{70}}{{POP}}\times 100 \% \end{array}\right..$$
(4)

We first evaluated differences in pension scales caused by extending retirement ages and subsequently assessed their impact on per capita GDP in both regions. However, given the unavailability of direct data on older-adult population size in coastal and interior areas, we employed the product of national ageing rates and the populations of coastal and interior areas to estimate the older-adult populations in these areas using the following equations:

$${Y}^{{SEA}}=\left\{\begin{array}{c}{{POP}}_{60}^{{SEA}}={\alpha }_{60}\times {{POP}}_{{SEA}}^{{SSP}}\\ {{POP}}_{65}^{{SEA}}={\alpha }_{65}\times {{POP}}_{{SEA}}^{{SSP}}\\ {{POP}}_{70}^{{SEA}}={\alpha }_{70}\times {{POP}}_{{SEA}}^{{SSP}}\end{array}\right.,$$
$${Y}^{{LAND}}=\left\{\begin{array}{c}{{POP}}_{60}^{{LAND}}={\alpha }_{60}\times {{POP}}_{{LAND}}^{{SSP}}\\ {{POP}}_{65}^{{LAND}}={\alpha }_{65}\times {{POP}}_{{LAND}}^{{SSP}}\\ {{POP}}_{70}^{{LAND}}={\alpha }_{70}\times {{POP}}_{{LAND}}^{{SSP}}\end{array}\right..$$
(5)

Ultimately, the calculated older-adult population sizes for both the coastal and interior areas were summed to determine the global scale of the populations in these regions under different SSPs.

From the perspective of the physical economy, regardless of the nature of the pension system, the provision of pensions involves allocating a certain portion of economic output for older-adult care.

Assuming total economic output is denoted as \(Y(t)\), where t represents time, and the portion allocated to pension funds is represented by \({Y}_{r}(t)\), by referencing the definitions and formulas derived in The Economic Effects of an Aging Population (Feenstra et al., 2015), we can derive the following expression:

$${Y}_{r}(t)=\theta \alpha Y(t),$$
(6)

where \(\theta\) represents the pension level coefficient, and \(\alpha\) denotes the population aging rate. The proportion of pension funds over GDP is given by \({Y}_{r}(t)/Y(t)\), which is expressed as:

$${Y}_{r}(t)/Y(t)=\theta \alpha .$$
(7)

The critical point \({\theta }_{0}\) at which population aging affects economic growth is

$$\theta =1/\left(\frac{1}{\partial }\cdot \frac{s\left(1-\alpha \right)}{\beta }+\alpha\right).$$
(8)

Here, when a proportion of GDP higher than \(\theta\) is allocated to pensions, population aging exerts a negative effect on economic growth. \(\partial\) represents the ratio of a country’s capital stock (CS) to annual GDP, S denotes the national saving rate, and \({\rm{\beta }}\) is the aging population average growth rate. In order to validate the pension coefficient (\(\theta\)), we compared our model’s projections with historical pension-to-GDP ratios in Japan—one of the world’s most rapidly aging societies. According to OECD (2021) and the Ministry of Health, Labour and Welfare of Japan (2022), Japan’s total social security expenditures reached 138.7 trillion yen in 2021, accounting for 35.04% of national income. Our model estimates indicate that, assuming a retirement age of 60, Japan’s pension coefficient in 2030 would be approximately 42% under SSP1, 38% under SSP2, 38% under SSP3, 41% under SSP4, and 37% under SSP5. These figures show a gradual increase over time and are broadly consistent with Japan’s historical trend of rising pension expenditures. This comparison enhances confidence in our model’s internal assumptions and supports the reliability of \(\theta\) in capturing the evolution of pension burdens across different scenarios.

To proceed with analysis, data on the ratio of CS to annual GDP for different countries were required. We utilised the ‘capital detail’ data from the Penn World Table (version 10.01) to ensure standardised computation (Romer, 1999) based on the following equation:

$${\partial }_{i}={{CS}}_{i}/{{GDP}}_{i}.$$
(9)

Empirical economic data indicate that the ratio of a country’s CS to output level tends to remain relatively stable. In most countries, the value of \(\partial\) is typically approximately two to three, (e.g. the US CS value is approximately 2.5 times annual GDP). Similarly, macroeconomic model estimations by the Institute of Quantitative and Technical Economics at the Chinese Academy of Social Sciences indicate that the ratio \(\partial\) of capital stock to annual GDP in China is approximately 2.2. Our calculated data yielded a value of approximately 2.0, demonstrating consistency between datasets.

The next step was to calculate the average growth rate of population ageing using the following equation:

$$\beta =\frac{1}{10}\cdot\frac{{\alpha }_{i}-{\alpha }_{j}}{{\alpha }_{i}},$$
(10)

where \({\alpha }_{i}\) represents the baseline year population aging rate and \({\alpha }_{j}\) signifies the population aging rate for the next period (10 years). After calculating the pension level coefficient \(\theta\), we proceeded to calculate the pension standard for sample countries, representing the pension amount accessible to each older adult (\(\complement\)) as follows:

$$\complement =\frac{{GDP}}{{POP}}\cdot \theta .$$
(11)

The pension expenditure scale for the coastal area is

$$\left\{\begin{array}{c}{Y}_{60}^{{SEA}}={Y}_{1}^{{SEA}}\cdot {\complement }_{1}+{Y}_{2}^{{SEA}}\cdot {\complement }_{2}+\cdots +{Y}_{N}^{{SEA}}\cdot {\complement }_{N}\\ {Y}_{65}^{{SEA}}={Y}_{1}^{{SEA}}\cdot {\complement }_{1}+{Y}_{2}^{{SEA}}\cdot {\complement }_{2}+\cdots +{Y}_{N}^{{SEA}}\cdot {\complement }_{N}\\ {Y}_{70}^{{SEA}}={Y}_{1}^{{SEA}}\cdot {\complement }_{1}+{Y}_{2}^{{SEA}}\cdot {\complement }_{2}+\cdots +{Y}_{N}^{{SEA}}\cdot {\complement }_{N}\end{array}\right.,$$
(12)

and that for the interior area is

$$\left\{\begin{array}{c}{Y}_{60}^{{LAND}}={Y}_{1}^{{LAND}}\cdot {\complement }_{1}+{Y}_{2}^{{LAND}}\cdot {\complement }_{2}+\cdots +{Y}_{N}^{{LAND}}\cdot {\complement }_{N}\\ {Y}_{65}^{{LAND}}={Y}_{1}^{{LAND}}\cdot {\complement }_{1}+{Y}_{2}^{{LAND}}\cdot {\complement }_{2}+\cdots +{Y}_{N}^{{LAND}}\cdot {\complement }_{N}\\ {Y}_{70}^{{LAND}}={Y}_{1}^{{LAND}}\cdot {\complement }_{1}+{Y}_{2}^{{LAND}}\cdot {\complement }_{2}+\cdots +{Y}_{N}^{{LAND}}\cdot {\complement }_{N}\end{array}\right..$$
(13)

We then calculated real GDP (RGDP) for the coastal and interior areas after deducting pension expenditures as follows:

$$\left\{\begin{array}{c}{{RGDP}}_{60}^{{SEA}}={{GDP}}_{{SEA}}^{{SSP}}-{Y}_{60}^{{SEA}}\\ {{RGDP}}_{65}^{{SEA}}={{GDP}}_{{SEA}}^{{SSP}}-{Y}_{65}^{{SEA}}\\ {{RGDP}}_{70}^{{SEA}}={{GDP}}_{{SEA}}^{{SSP}}-{Y}_{70}^{{SEA}}\end{array}\right.,$$
$$\left\{\begin{array}{c}{{RGDP}}_{60}^{{LAND}}={{GDP}}_{{LAND}}^{{SSP}}-{Y}_{60}^{{LAND}}\\ {{RGDP}}_{65}^{{LAND}}={{GDP}}_{{LAND}}^{{SSP}}-{Y}_{65}^{{LAND}}\\ {{RGDP}}_{70}^{{LAND}}={{GDP}}_{{LAND}}^{{SSP}}-{Y}_{70}^{{LAND}}\end{array}\right..$$
(14)

We substituted the RGDP values for the coastal and interior areas obtained from Eq. (14) into Eqs. (2) and (3). We then recalculated the disparity in RGDP per capita between both regions and assessed the regional development balance, as well as the impact of postponing retirement age on their balanced development. To ensure the continuity and comparability of this research, the ratio of CS to annual GDP (\({\partial }_{i}\)) and national saving rate were held constant using data from 2018 as fixed values. By holding these factors constant, we isolated the effects of changes in the proportion of pension scale to GDP resulting from fluctuations in population ageing and conducted regional comparisons accordingly.