County-level estimates of population and economic scenarios under the shared socioeconomic pathways: A case study in Inner Mongolia, China
Introduction
The increase of the greenhouse gases (GHG) concentration caused by human activities is considered to be the main factor leading to global warming (IPCC, 2013). In the past half century, the rapid growth of population and economy has brought unprecedented challenges to the climate change (Jiang and Hardee, 2011; Tol, 2018). At the same time, population and economic development also determine the choice of climate change countermeasures (Lozano and Gutierrez et al., 2008; Satterthwaite et al., 2009; Wu and Han, 2020). The dynamic combination between population and economic changes plays an important role in the formulation of measures to control GHG emissions and climate change policy on climate change mitigations and adaptations (Diaz& Moore, 2017; Doelman et al., 2018; Cradock-Henry et al., 2018).
Developing new socio-economic development scenarios have become one of the core issues in the study of climate change and climate change impact (Kriegler et al., 2010). As an important part of the latest IPCC scenarios, the Shared Socioeconomic Pathways (SSPs) play an important role in assessing climate change impacts and supporting climate policy decisions. The SSPs describe five different future scenarios, describing the relationship between climate change and socio-economic elements, and reflecting the adaptation and mitigation challenges of climate change in different socioeconomic development. Among them, SSP1 is a sustainable path with relatively high-speed technology transformation. Governments and institutions are committed to achieving development goals and solving problems to reduce vulnerability to climate change. SSP2 is an intermediate path with the current speed of development, making some progress in energy, science and technology. The income gap is slowly narrowing between developing and industrialized countries under SSP2. SSP3 is a path for regional competition. Under SSP3, each country focuses on its own energy and food security, with weak international cooperation and reduced investment in technological development and education. As a result, a large number of population and economy are vulnerable to the impact of climate change and have low adaptability to climate change. Low technological change in the energy sector results in large carbon emissions under SSP3. SSP4 is an unbalanced path, in which wealthy groups produce the majority of emissions, while large numbers of poor groups in developing countries emit less and are vulnerable to climate change. As a fossil-fuel-based development path, SSP5 brings a large amount of GHG emissions and faces great mitigation challenges and relatively low challenges of social and environmental adaptation (Riahi et al., 2017).
Demographic and economic changes are the first factors to be considered in the socio-economic development scenarios (Price et al., 2017). At present, existing studies have carried out population and economic projections under different SSPs at the global and regional/national scales (Merkens et al., 2018; Kebede et al., 2018; Nepal et al., 2019; Hertel et al., 2019; Korhonen et al., 2020). Samir and Lutz (2017) simulated the changes of population and human capital over the period of 2010–2100 based on the assumptions of future fertility, mortality, migration, and education under five SSP storylines. In terms of economic projection, the International Institute for Applied Systems Analysis (IIASA), the Potsdam Institute for Climate Impact Research (PIK) and the Organization for Economic Co-operation and Development (OECD) carried out economic development projections for more than 190 countries under different SSPs (Leimbach et al., 2017; Dellink et al., 2017; O ‘neill et al., 2017). However, most of the current studies on simulation analysis and application under SSPs adopts the scenario data in the global SSP scenario database and ignores the detailed extension of the SSP scenario at the sub-national (provincial/municipal/county) level, which is difficult to meet the scenario requirements for regional research (Dong et al., 2018). For China, the parameter correction based on China's actual situation and relevant policies has been ignored. Specifically, current studies fail to consider the change of labor induced by China's current "two-child" policy and its possible impact on economic development (Huang et al., 2019). Economic projections should take into account changes of the labor caused by changes of population policies in China (Cai et al., 2016; Liu et al., 2017; Ge et al., 2018).
On the other hand, the growing demand for small regional demographic analyses, particularly for those analyses related to climate change, highlights the importance of sub-national scale projections. Small area and subnational population projections is important for understanding long-term demographic changes (Jones & O'Neill, 2016; Hauer, 2019). However, there are still technical bottlenecks in regional county-level projection under SSPs (Hundessa et al., 2018). Due to the lack of rigorous small-area population projections by detailed demographic subgroups, our understanding of subnational demographic change in China is hampered (McLennan, 2016). Therefore, based on the five SSP storylines proposed by IPCC, we proposed a technical solution for localization, parameterization and spatialization of SSP scenarios at county level. We carried out population and economic projections of 101 counties in Inner Mongolia, China during 2010–2050 under five SSPs as a demonstration. The results provide technical support for population and economic scenarios under SSPs at a small regional level, and provides scientific basis for the formulation of climate change policies in order to formulate measures to deal with climate change risks.
Section snippets
Study area
Inner Mongolia, China is located in the middle of the Eurasian continental steppe belt. It extends from northeast to southwest in a long and narrow shape, with a direct distance of 2400 km from east to west and 1700 km from north to south. It crosses three regions of northeast China, north China and northwest China (Fig. 1). The total area of Inner Mongolia, China is 1.2 million km2, accounting for 12.3% of China's total area. It is the most widely distributed natural ecological system in the
Methodology
Based on the data of the sixth population census of Inner Mongolia in 2010, we adopt five SSP storylines combined with the actual situation and relevant policies of Inner Mongolia to calibrate the fertility rate, mortality rate, migration rate, education and other parameters in the Population Development Environment (PDE) model and then estimate the population change of 101 counties in Inner Mongolia, China under different SSPs during 2011–2050. Then, we apply Cobb-Douglas (C-D) model to
Population projection
Fig. 5 shows the estimated results of the total population of Inner Mongolia from 2010 to 2050 under five SSPs. The study found that the population increased first and then decreased under five SSPs. On the whole, the total population of Inner Mongolia under five SSPs are ranked as SSP3> SSP2> SSP5> SSP1> SSP4. Among them, the population of SSP2, SSP3 and SSP5 will peak in 2035, respectively reaching 25.94 million, 26.26 million and 25.49 million, with an increase of 1.21 million, 1.55 million
Conclusions
Combing the climate change scenario released by IPCC and China's current population policy, considering the actual social and economic development of Inner Mongolia, this study localizes the key scenario parameters such as fertility rate, mortality rate, migration rate, and education level and uses PDE model to simulate population changes of 101 counties in Inner Mongolia from 2010 to 2050 under the shared social-economic pathways. By calibrating key scenario parameters such as labor input,
Author statement
Yuping Bai did modelling and writing- original draft preparation work.
Wenxuan Wang did data collection and processing.
Fan Zhang designed the structure of this paper and went through all sectional works.
Zehao Wang revised the paper and drew the graphs and charts.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
The research is supported by the Fundamental Research Funds for the Central Universities (2-9-2020-022), the National Key Research and Development Program of China (Grant No. 2016YFA0602500) and the Open-ended Fund of Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences (Grant No. LB2021001). Data support from National Natural Science Fund for Distinguished Young Scholar is also appreciated (Grant No. 71225005).
References (36)
- et al.
Long-term economic growth projections in the shared socioeconomic pathways
Global Environ. Change
(2017) - et al.
Exploring SSP land-use dynamics using the IMAGE model: regional and gridded scenarios of land-use change and land-based climate change mitigation
Global Environ. Change
(2018) - et al.
Land use projections in China under global socioeconomic and emission scenarios: utilizing a scenario-based land-use change assessment framework
Global Environ. Change
(2018) - et al.
Population policies, demographic structural changes, and the Chinese household saving puzzle
Eur. Econ. Rev.
(2018) - et al.
Does ‘Forage-Livestock Balance’policy impact ecological efficiency of grasslands in China?
J. Clean. Prod.
(2019) - et al.
Projecting potential spatial and temporal changes in the distribution of Plasmodium vivax and Plasmodium falciparum malaria in China with climate change
Sci. Total Environ.
(2018) - et al.
Applying the global RCP–SSP–SPA scenario framework at sub-national scale: a multi-scale and participatory scenario approach
Sci. Total Environ.
(2018) - et al.
Future growth patterns of world regions: a GDP scenario approach[J]
Global Environ. Change
(2017) - et al.
The impact of urbanization on GHG emissions in China: the role of population density
J. Clean. Prod.
(2017) - et al.
Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions
Ecol. Econ.
(2008)
The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century
Global Environ. Change
The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview
Global Environ. Change
Sectoral changing patterns of China's green GDP considering climate change: an investigation based on the economic input-output life cycle assessment model
J. Clean. Prod.
Assessment of climate change adaptation measures on the income of herders in a pastoral region
J. Clean. Prod.
Take‐off, persistence and sustainability: the demographic factor in Chinese growth
Asia Pac. Pol. Stud.
Dynamic adaptive pathways in downscaled climate change scenarios
Climatic Change
Quantifying the economic risks of climate change
Nat. Clim. Change
Population projections for US counties by age, sex, and race controlled to shared socioeconomic pathway
Sci. Data
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