<|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:"How socioeconomic and environmental factors impact the migration destination choices of different population groups in China: an eigenfunction-based spatial filtering analysis."
Abstract:"Understanding how different factors impact migration destination choices is one of the main research themes in demographic studies. The current study uses relative intrinsic attractivity (RIA) as a measure for a place's attractivity and attempts to apply an eigenfunction-based spatial filtering (ESF) approach to investigate the relationships between a place's attractivity and 12 socioeconomic and natural condition factors in China at the prefecture level. Results suggest that the ESF approach may provide a potentially more robust way to account for how various factors impact different groups of people's migration destination choices than non-spatial and spatial autoregressive models. The ESF approach is able to adequately address the spatial effects on data analysis when using geographic data and provide easily interpretable results. Places with better accessibility by roads, better economic opportunities (jobs and wages), and cooler average annual temperature are more attractive to all subgroups of migrants. Different sub-groups of migrants, however, are also attracted to places with different priorities and characteristics. The current study uses an ESF approach for the first time to investigate how factors impact different groups of people's migration destination choices.."
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 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [2,3];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [1].
How socioeconomic and environmental factors impact the migration destination choices of different population groups in China: an eigenfunction-based spatial filtering analysis
Understanding how different factors impact migration destination choices is one of the main research themes in demographic studies. The current study uses relative intrinsic attractivity (RIA) as a measure for a place's attractivity and attempts to apply an eigenfunction-based spatial filtering (ESF) approach to investigate the relationships between a place's attractivity and 12 socioeconomic and natural condition factors in China at the prefecture level. Results suggest that the ESF approach may provide a potentially more robust way to account for how various factors impact different groups of people's migration destination choices than non-spatial and spatial autoregressive models. The ESF approach is able to adequately address the spatial effects on data analysis when using geographic data and provide easily interpretable results. Places with better accessibility by roads, better economic opportunities (jobs and wages), and cooler average annual temperature are more attractive to all subgroups of migrants. Different sub-groups of migrants, however, are also attracted to places with different priorities and characteristics. The current study uses an ESF approach for the first time to investigate how factors impact different groups of people's migration destination choices.