TY - JOUR
T1 - Analysis of spatial parameter formulation in the effect of Green House Gas (GHG) emissions on food security in Surabaya
AU - Kurniawati, U. F.
AU - Idajati, H.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/10/7
Y1 - 2019/10/7
N2 - Surabaya is an urban area that has a population growth rate increasing every year, which is 0.52% in 2010-2013 and increased to 0.55% in 2013-2014. Increasing population growth has an impact on increasing urban food availability. Identification of food availability in Surabaya consists of agriculture and livestock sectors, namely rice production (X1), corn production (X2), cassava production (X3), beef cattle production (X4), dairy cow production (X5), buffalo production (X6), goat production (X7), horse production (X8), and poultry production (X9). Data reduction in sub-districts that have no contribution to the agricultural and livestock sector emissions results in 15 sub-district input data that have a contribution. The spatial regression process. Significance test on the independent variable shows global variables (G), namely rice production (X1), corn production (X2), cassava production (X3), dairy cow production (X5), buffalo production (X6), goat production (X7), and poultry production (X9). While the independent variables that show local variables (L) are beef cattle production (X4) and horse production (X8).
AB - Surabaya is an urban area that has a population growth rate increasing every year, which is 0.52% in 2010-2013 and increased to 0.55% in 2013-2014. Increasing population growth has an impact on increasing urban food availability. Identification of food availability in Surabaya consists of agriculture and livestock sectors, namely rice production (X1), corn production (X2), cassava production (X3), beef cattle production (X4), dairy cow production (X5), buffalo production (X6), goat production (X7), horse production (X8), and poultry production (X9). Data reduction in sub-districts that have no contribution to the agricultural and livestock sector emissions results in 15 sub-district input data that have a contribution. The spatial regression process. Significance test on the independent variable shows global variables (G), namely rice production (X1), corn production (X2), cassava production (X3), dairy cow production (X5), buffalo production (X6), goat production (X7), and poultry production (X9). While the independent variables that show local variables (L) are beef cattle production (X4) and horse production (X8).
UR - http://www.scopus.com/inward/record.url?scp=85073692221&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/340/1/012041
DO - 10.1088/1755-1315/340/1/012041
M3 - Conference article
AN - SCOPUS:85073692221
SN - 1755-1307
VL - 340
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012041
T2 - 2018 CITIES International Conference: Spatial Economic Transport Interaction for Sustainable Development
Y2 - 24 October 2018 through 25 October 2018
ER -