文章基本信息

标题：Implications of Fitting a Regression and Pearson Correlation Models in the Relationship Between Food Production, Production of Wood Products, CO₂Emissions and Climate: An Analysis of Time Series Data
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作者：Epule Terence Epule ; Changhui Peng ; Laurent Lepage 等
期刊名称：Research Journal of Environmental Sciences
印刷版ISSN：1819-3412
电子版ISSN：2152-8238
出版年度：2013
卷号：7
期号：1
页码：1-14
DOI：10.3923/rjes.2013.1.14
出版社：Academic Journals Inc., USA
摘要：This study investigates the most significant determinants of food production in Canada from among the following variables: Production of wood products, CO₂ emissions from agriculture and forestry, CO₂ emissions from fossil fuels, rainfall and temperature. It also verifies the relationship between food production, production of wood products, CO₂ emissions from agriculture and forestry, CO₂ emissions from fossil fuels, rainfall and temperature in Canada. The data for the analysis was essentially time series data spanning the period 1961-2010. The data on food production and production of wood products was obtained from FAOSTAT. The data on CO₂ emissions from agriculture and forestry and fossil fuels was obtained from FAOSTAT and the US Department of Energy. The data on rainfall and temperature was obtained from Environment Canada, 2012. Data analysis was performed in the SPSS platform in which both multiple linear regression and bivariate linear correlations were used to fit the models. The results show that CO₂ emissions generally increase with food production. Food production and CO₂ emissions from agriculture and forestry have a correlation of about 0.87 while food production at the same time has a correlation of 0.84 with CO₂ emissions from fossil fuels. Both CO₂ emissions from agriculture and forestry and fossil fuels have a correlation of 0.94 which shows that they reinforce each other. The most significant variable that significantly correlates with food production is CO₂ emissions from agriculture and forestry with a t-value of 2.63 and a p-value of 0.12.