<|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 food waste under the FUSIONS definition: What are the driving factors of food waste in the Italian provinces?."
Abstract:"Following the definition provided by the EU-funded FUSIONS project (food use for social innovation by optimising waste prevention strategies), this work estimates food waste at the provincial level (NUTS-3) in Italy, using official records on separate waste collection over the 2004-2014 time span. Moreover, it sheds some light on how economic and socio-demographic characteristics affect food waste levels and composition. We run a fixed effects panel regression taking into account a number of socio-economic factors. Our results show that food waste is positively affected by population density and consumption levels, while it is negatively affected by the share of women, elderly people, immigrants and unemployed in the population. We also analyse the components of food wastethe one resulting from unsorted waste and the one resulting from separated collectionand we show how they are affected by socio-economic drivers. Overall, our analysis highlights the role of consumption levels. Locally targeted awareness campaigns aiming to reduce food consumption need to be devised in order to tackle the food waste challenge.."
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].
Estimating food waste under the FUSIONS definition: What are the driving factors of food waste in the Italian provinces?
Following the definition provided by the EU-funded FUSIONS project (food use for social innovation by optimising waste prevention strategies), this work estimates food waste at the provincial level (NUTS-3) in Italy, using official records on separate waste collection over the 2004-2014 time span. Moreover, it sheds some light on how economic and socio-demographic characteristics affect food waste levels and composition. We run a fixed effects panel regression taking into account a number of socio-economic factors. Our results show that food waste is positively affected by population density and consumption levels, while it is negatively affected by the share of women, elderly people, immigrants and unemployed in the population. We also analyse the components of food wastethe one resulting from unsorted waste and the one resulting from separated collectionand we show how they are affected by socio-economic drivers. Overall, our analysis highlights the role of consumption levels. Locally targeted awareness campaigns aiming to reduce food consumption need to be devised in order to tackle the food waste challenge.