<|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:"Factors That Attract the Population: Empirical Research by Multiple Regression Analysis Using Data by Prefecture in Japan."
Abstract:"The development of the local economy and correcting the concentration in the capital city have long been the target for many countries. Furthermore, in the wake of the recent COVID-19 pandemic, the momentum for rural migration has been increasing to prevent the risk of infection with the help of the rise of remote work. However, there is not enough debate about what kind of land will attract the population. Therefore, in this paper, we performed correlation and multiple regression analyses, with the inflow rate and the net inflow rate of the population as the dependent variables, using the average values of government statistics for each prefecture in 2010 and 2017. As a result of the analyses, in addition to economic factor variables, variables of climatic, amenity, and human factors correlated with the inflow rate, and it was shown that the model had the greatest explanatory power when multiple factors had been used in addition to specific factors. It indicated that local prefectures were required to take regional promotion measures focusing not only on economic factors but also on multifaceted factors to attract the outside population. In addition, when the dependent variable was replaced with the 2020 population inflow rate, the model in which human factors were used as the independent variable showed the largest improvement in explanatory power. Therefore, it was shown that human factors have become more important in attracting people during the COVID-19 pandemic.."
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 = [3,6];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [1].
Factors That Attract the Population: Empirical Research by Multiple Regression Analysis Using Data by Prefecture in Japan
The development of the local economy and correcting the concentration in the capital city have long been the target for many countries. Furthermore, in the wake of the recent COVID-19 pandemic, the momentum for rural migration has been increasing to prevent the risk of infection with the help of the rise of remote work. However, there is not enough debate about what kind of land will attract the population. Therefore, in this paper, we performed correlation and multiple regression analyses, with the inflow rate and the net inflow rate of the population as the dependent variables, using the average values of government statistics for each prefecture in 2010 and 2017. As a result of the analyses, in addition to economic factor variables, variables of climatic, amenity, and human factors correlated with the inflow rate, and it was shown that the model had the greatest explanatory power when multiple factors had been used in addition to specific factors. It indicated that local prefectures were required to take regional promotion measures focusing not only on economic factors but also on multifaceted factors to attract the outside population. In addition, when the dependent variable was replaced with the 2020 population inflow rate, the model in which human factors were used as the independent variable showed the largest improvement in explanatory power. Therefore, it was shown that human factors have become more important in attracting people during the COVID-19 pandemic.