Projections of the spatial distribution of population are consequential for integrated human-environment analysis. Population and its spatial distribution are drivers of land-use/land-cover change [1
] and greenhouse gas emissions [3
], both with significant impacts on the climate, biodiversity, and air quality. Temporal changes of population also play a significant role in determining urbanization regimes and the extent to which human’s presence in the form of built-up areas is spatially distributed [4
]. Projections of the spatial distribution of population also help identify those that are likely to be most affected by climate change and other environmental stress, which can inform resource allocation, adaptation, and mitigation policies in relation to environmental hazards. For example, such projections have been used to find that the increasing likelihood of storm surge and coastal flooding ensuing from sea level rise [5
] may impact the considerable proportion of the world population that is already living close to coastal areas [6
]. Moreover, the combination of global warming and changes in the spatial distribution of the population driven by urbanization will lead to more people being exposed to severe heatwaves [8
]. Climate change can also influence the distribution of land area capable of supporting vector-borne diseases [10
], which can lead to greater human exposure [12
]. In addition to such projections that shed light on the population exposure to the detrimental effects of climate change, studies have started to augment them with supplementary demographic attributes such as income distribution and age structure to address challenges in vulnerability differentiation of the population [13
The use of alternative scenarios for future conditions of society and the environment is a common approach in human-environment analysis to address uncertainty. Each scenario represents a plausible and unique future formed by a collection of determining inputs, and the set of scenarios as a whole addresses uncertainty by incorporating all these distinct possibilities. The production of alternative spatial population projections, as elements of broader societal scenarios, has been an important component of this collective perspective on uncertainty. For the U.S. region, a previous study downscaled the U.S. population at the Census division level to 1/8° resolution grids consistent with the A2 and B2 socioeconomic scenarios from the Special Report on Emissions Scenarios (SRES) [15
]. Researchers in [16
] used U.S. Census-based projections to derive county-level population aggregates and downscaled them to grids in 2030 and 2050. Researchers in [17
] generated a series of global population projection grids by downscaling country level population projections under the Shared Socioeconomic Pathways (SSPs) [18
] for the 2010–2100 period. Other US projections consistent with the SSPs have been produced at the county level [19
] but not downscaled to the grid cell level.
SSPs are widely adopted in the human-environment analysis community as a set of qualitative narratives describing different societal development pathways with distinct implications in the societies’ capacity for adaption to or mitigation of climate change effects [18
]. The SSP narratives have been complemented by quantitative projections of several socioeconomic attributes reflecting distinct patterns of national-level population growth and educational composition [22
], urbanization level [23
] and economic growth [24
]. Combined with climate models, SSP projections offer a widely-used, comprehensive framework to assess the mutual effects between climate change and society [25
]. They also play a crucial role in enhancing the effectiveness of adaptation and preparedness programs in relation to environmental hazards by identifying the most vulnerable segments of the society.
The SSPs have been defined to formulate challenges that societal conditions would present to adaptation to climate change and mitigation of emissions. However, they are not subtle enough in their original form to formulate future socioeconomic conditions at local or regional scales. To foster incorporating the SSP framework across specific domains and geographic scales, their global definitions need to be extended and encompass local or regional subtleties that are eclipsed in the global version. Previous studies have already highlighted the importance of this topic and developed and applied extended SSPs to specific applications such as population dynamics in coastal areas and determinants of human vulnerability in Europe [26
This study follows an ongoing research endeavor whose objective is to provide different SSP-based demographic attributes specific to each U.S. state, by which their associated uncertainty is considered. It generates projections of spatial population distribution for each state for the 2020–2100 period by downscaling their population aggregates to 1 km resolution grids. The downscaling employs a gravity-based model tailored to each state, which is documented in [29
] and is based on previous work for generating scenario-based population distributions [15
]. State-level population aggregates draw on [31
], who extend national-level SSP2, SSP3, and SSP5 assumptions of fertility, mortality, and migration to each state and project population by age and sex for each state as a whole accordingly using a demographic cohort component model (Appendix A.1
In the following section, we describe the SSPs and explain how we implement their corresponding assumptions about total population, urbanization, and spatial development patterns. We then describe our methodology for producing spatial projections for the U.S. by summarizing the gravity model structure and detailing the approach we use to modify its parameters to produce development patterns consistent with the SSPs. We then present our spatial population projection results according to the SSPs and discuss their distinguishing features. We conclude this paper by summarizing the lessons learned through the analysis and potential areas of improvement.
2. SSPs and Their Demographic Assumptions
The SSPs consist of five alternative socioeconomic pathways that societies could follow in the future, in terms of their demographics, human development, economy, lifestyle, policies, technology, and environment and natural resources, which have significant implications for their climate-related adaptation and mitigation capacities. SSP1(Sustainability) describes a development path that emphasizes environmental protection, reduced inequality, and significant investment in education and health. SSP2 (Middle of the Road) envisions a world whose various aspects of socioeconomic development resemble historical trends. SSP3 (Regional Rivalry) leads to a world with a dominant presence of nationalism and security concerns, resulting in regionalization, slow economic growth, and low investment in human capital and education. SSP4 (Inequality) is similar to SSP3 in terms of neglecting sustained investment in human development but differs in that it depicts a world with a widening gap between the majority of the society and a small, well-educated, and internationally connected elite class, within countries as well as across them. SSP5 (Fossil-fueled Development) envisions a world with substantial focus on globalization, international competitive markets, and rapid economic growth that produce high levels of human development. However, it relies on fossil fuels and lacks sustainable environmental protection policies exercised in SSP1.
Three factors determine spatial population projections in each state, namely its population aggregate, urbanization level, and spatial distribution pattern. We, therefore, employ assumptions on these determinants conforming to three of the SSPs (SSP2, SSP3, and SSP5) to incorporate uncertainty and inform our spatial population projections. We choose these SSPs based on the availability of state-level population and urbanization projections for each of them, as well as the fact that they span the full range of national population size across the SSPs and have a diversity of assumptions about spatial development patterns. For population, we use the quantitative results of recent state-level projections from [31
], who use a demographic cohort component model to generate state-level population aggregates based on assumptions on future fertility, mortality, domestic, and international migration in each state that are consistent with the SSPs (Appendix A.1
). In summary, at the national level the three SSPs lead to a range of U.S. population from ~246 to 650 million by 2100 (Table A1
and Figure A1
), a range that is somewhat lower than the range in the original SSP population projections (~451 million) [22
] due primarily to updated base year data showing lower current levels of fertility and migration ([31
] and Appendix A.1
SSP2 leads to moderate population changes reflecting historical fertility, mortality, and migration trends. Due to the high income levels, investments in human capital, and relatively open borders that SSP5 envisions, this scenario is associated with the highest fertility and migration and lowest mortality projections, resulting in the highest population growth. Conversely, SSP3 is a scenario of low income growth, relatively low investment in human capital, and limited international flows, which translate to low fertility and migration, resulting in the slowest population growth and even population decline in some states over the course of the projection period. The projections assume that domestic migration between states retain the currently observed regional pattern, but the overall scale of that migration is highest in SSP5 and lowest in SSP3. The projections lead to substantial heterogeneity in population growth across states, ranging for example from substantial decline in some states and SSPs (e.g., the Northeast in SSP3) to substantial growth in others (e.g., Utah and Texas in SSP5), covering uncertainty in different pathways that the population of states might take.
For urbanization, we use SSP-based projections of the share of urban population by state from a model based on observed trends in urbanization patterns at the national and sub-national level [32
], summarized and illustrated in Appendix A.2
and Figure A2
). The rapid economic growth according to SSP5 produces large urban employment opportunities and high migration rates. Therefore, urbanization grows rapidly in this scenario, and large numbers of people move to cities. In contrast, the economic uncertainty associated with SSP3 manifests itself in a paucity of employment opportunities in urban areas and low migration rates. These two features contribute to slow urbanization in this scenario. SSP2, as a moderate scenario that is situated in the middle of the spectrum, envisions moderate urbanization.
Regarding spatial development patterns, we adopt the qualitative assumptions in the SSP narratives to support our modeling choices [17
]. SSP5 leads to rapid economic and urbanization growth. However, sustainable planning in this scenario is not prioritized as opposed to SSP1. Thus, urban planning cannot keep up with the large influx of population, leading to a sprawling spatial distribution of population extending from cities. On the other hand, urban areas fail to attract population as strongly in SSP3. Combined with the lack of planning in this scenario, this results in a spatial distribution of population which is neither consolidated nor sprawling but rather a mixed pattern. SSP2 conforms to the historical spatial distribution of population and depending on the area, it can follow consolidation, sprawl or mixed.
In this paper, we downscaled U.S. state level population aggregates to their constituent grid cells. To demonstrate uncertainty in projections of the spatial distribution of population, we produced them based on three alternative scenarios, namely SSP2, SSP3, and SSP5, that are widely used in human-environment dynamics analysis. Each scenario envisions an alternative socioeconomic path that societies might follow with distinct implications for their capacity to respond to climate change. In addition to differences in population aggregates and urbanization levels that these scenarios entail, each leads to an alternative spatial pattern of population distribution. We used a semantic framework that interprets quantitative values of spatial population model parameters in qualitative terms to design spatial projections that are consistent with the narrative descriptions of intended development patterns in the SSPs.
We presented population projection grids consistent with the three SSPs at both national and state levels in 2050 and 2100. These grids depict spatial distributions of population that align with our expectation from each SSP. SSP5 leads to the most dominant population sprawl pattern, whereas SSP3 results in the most dispersed distribution with the lowest growth in populous urban areas. SSP2, as a moderate scenario, leads to distributions in the middle of the divide in terms of overall urbanization but that emphasizes concentrated growth. Comparing projections normalized for different total populations at the national and state levels showed that our SSP-based projections differences are driven to a substantial extent by differences in spatial patterns of development within states.
Finally, we compared our spatial population projections with the projections from a global model that downscaled national (as opposed to state-level) population totals [17
]. The differences show that spatial patterns consistent with each SSP are more pronounced in the state-level model used in this paper, thereby providing a wider coverage of uncertainty. There are also disparities in outcomes for population across broad regions, due to the ability of the state-level model to account for migration between states and heterogeneity in other demographic rates. Population is generally smaller in our projections compared to the global model in New England and California, where regional fertility and domestic migration are projected to be low.
Global spatial projections such as those from [17
] are valuable because they use a methodology and are based on data that can be applied consistently across all countries of the world. We believe that projections such as ours offer improved country-specific results because they can take advantage of more and higher resolution data for a specific country that may not be available elsewhere, draw on subnational aggregate projections (such as for states) that can capture demographic heterogeneity, and estimate parameters that vary across subnational units.
However, future work could improve on this projection and others like it. Regarding the spatial model itself, obtaining a longer time series of past spatial population data would allow for parameter estimation over a longer period, more consistent with the long-term projections to which the model is applied. Although parameters are estimated separately for states and urban and rural populations, further differentiation would likely improve model performance and its flexibility for projecting future patterns, including distinguishing spatial patterns around larger and smaller cities and towns, and differentiating within rural areas between very low density areas and rural areas that are closer to suburban developments. Regarding scenario definition, testing the robustness or projections of alternative but equally plausible specifications of parameter values consistent with qualitative storylines, including their changes over time, would be valuable.
Our future work will complete the SSP projections for SSP1 and SSP4 to generate a series of state-level population distributions consistent with all five SSPs. We also plan to incorporate age structure into our spatial projections [13
]. These developments, combined with integration with alternative climate projections, will enable more effective analysis of questions about the exposure and vulnerability of the U.S. population to environmental hazards such as sea level rise and heat waves in the future.