County-level estimates of population and economic scenarios under the shared socioeconomic pathways: A case study in Inner Mongolia, China

Highlights

• Population and GDP of 101 counties in Inner Mongolia are predicted under SSPs.

• Population and GDP are high to low from east to west and south to north.

• The largest gap of population is 2.35 million between SSP3 and SSP4 in 2050.

• The largest gap of GDP is 1.5 trillion between SSP4 and SSP5 in 2050.

• The spatial distribution of GDP is positively related with the population.

Abstract

Contribution from socio-economic development to climate change cannot be ignored. In order to make a more reasonable comprehensive assessment of the future climate change, the Intergovernmental Panel on Climate Change (IPCC) has proposed the Shared Socioeconomic Pathways (SSPs) based on the background of climate change and the possible future socio-economic conditions, which describes the adaptation and mitigation challenges of climate change. Based on different SSP scenarios, combined with China's current population policy and the actual social and economic development in Inner Mongolia, this paper adopts PDE model and C-D model to simulate population and economic changes of various counties in Inner Mongolia from 2010 to 2050. It is founded that population and GDP varies from different counties significantly, and shows the patterns of high in east and low in west, high in south and low in north. The spatial distribution of GDP is positively related with the population. The total population increases first and then decreases from 2015 to 2050 in Inner Mongolia. By the ends of 2050, the population reaches 23.32 million (−9.46%), 24.14 million (−2.30%), 24.72 million (+0.05%), 22.37 million (−9.46%), 23.49 million (−4.93%) under SSP1, SSP2, SSP3, SSP4, SSP5 scenarios. The largest gap of population is 2.35 million between SSP3 and SSP4. The GDP grows constantly from 2010 to 2050 in Inner Mongolia, but the growth rate is slowing down. By the ends of 2050, GDP reaches 5.09 trillion (+4.75 times), 5.22 trillion (+4.90 times), 4.41 trillion (+3.98 times), 4.98 trillion (+4.62 times), 5.87 trillion (+5.63 times). Therein, the biggest gap of GDP is 1.5 trillion between SSP4 and SSP5. The results provide technical solution for population and economic projection under SSPs at small area and subnational level, and provides scientific basis for the formulation of climate change policies in order to formulate measures to deal with climate change risks.

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.