<|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:"China?s relocation policy for poverty reduction may save thousands of lives attributable to indoor PM2.5."
Abstract:"To complete the last stretch of complete poverty eradication by 2020, Chinese policymakers relocated about 10 million rural people from poverty-stricken regions closer to urban areas, between 2016 and 2020. Improved indoor air from transiting out of biomass usage to cleaner fuel for cooking and heating has large positive health externalities. Employing a spatiotemporal Gaussian process regression model based on the reviewed indoor PM2.5 measurements, we estimated the provincial urban/rural indoor PM2.5 concentrations and the related avoidable deaths among the relocated population. Results show that this policy implementation avoided 5.4 (95% uncertainty interval: 3.6-7.0) thousand deaths as a direct result of reductions in indoor PM2.5 concentration.."
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 = [2];
Q4 = [12];
Q5 = [1];
Q6 = [1].
China?s relocation policy for poverty reduction may save thousands of lives attributable to indoor PM2.5
To complete the last stretch of complete poverty eradication by 2020, Chinese policymakers relocated about 10 million rural people from poverty-stricken regions closer to urban areas, between 2016 and 2020. Improved indoor air from transiting out of biomass usage to cleaner fuel for cooking and heating has large positive health externalities. Employing a spatiotemporal Gaussian process regression model based on the reviewed indoor PM2.5 measurements, we estimated the provincial urban/rural indoor PM2.5 concentrations and the related avoidable deaths among the relocated population. Results show that this policy implementation avoided 5.4 (95% uncertainty interval: 3.6-7.0) thousand deaths as a direct result of reductions in indoor PM2.5 concentration.