<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Estimating China's Population over 21st Century: Spatially Explicit Scenarios Consistent with the Shared Socioeconomic Pathways (SSPs)."
Abstract:"Accurate and reliable subnational and spatially explicit population projections under shared socioeconomic pathways (SSPs) for China will be helpful for understanding long-term demographic changes and formulating targeted mitigation and adaptation policies under climate change. In this study, national and provincial populations for China by age, sex, and education level to 2100 under five SSPs were estimated using the population-development-environment model. These parameters include fertility, mortality, migration, and education and consider the most recent birth policy in China. To quantify these projections spatially, the gridded population was provided at 1 km x 1 km by spatial downscaling. Results show the national population is highest under SSP3, with 1.71 x 10(9) people in 2100. Guangdong, Henan, and Shandong are the most populous in SSP1, 2, 4, 5, while Guangxi is the most populous province in SSP3, reaching 1.54 x 10(8) people. The differences in education level among scenarios are obvious, especially in 2100 where education level for SSP1 and SSP5 is the highest. The spatial distribution of population varies across the country, with the majority of the population concentrated in southern and eastern China, especially in the coastal regions. Our results under different SSPs could provide a reference to project disaster risks, formulate relevant policies and guide sustainable development from a long-term perspective.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
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Q5 = [1];
Q6 = [1].
Estimating China's Population over 21st Century: Spatially Explicit Scenarios Consistent with the Shared Socioeconomic Pathways (SSPs)
Accurate and reliable subnational and spatially explicit population projections under shared socioeconomic pathways (SSPs) for China will be helpful for understanding long-term demographic changes and formulating targeted mitigation and adaptation policies under climate change. In this study, national and provincial populations for China by age, sex, and education level to 2100 under five SSPs were estimated using the population-development-environment model. These parameters include fertility, mortality, migration, and education and consider the most recent birth policy in China. To quantify these projections spatially, the gridded population was provided at 1 km x 1 km by spatial downscaling. Results show the national population is highest under SSP3, with 1.71 x 10(9) people in 2100. Guangdong, Henan, and Shandong are the most populous in SSP1, 2, 4, 5, while Guangxi is the most populous province in SSP3, reaching 1.54 x 10(8) people. The differences in education level among scenarios are obvious, especially in 2100 where education level for SSP1 and SSP5 is the highest. The spatial distribution of population varies across the country, with the majority of the population concentrated in southern and eastern China, especially in the coastal regions. Our results under different SSPs could provide a reference to project disaster risks, formulate relevant policies and guide sustainable development from a long-term perspective.