Open AccessMethods for predicting the negative impact on the environment during the operation of vehicles in agricultural production
Author(s) -
Е. В. Тимофеев,
А Ф Эрк,
T I Gordeeva
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/979/1/012112
Subject(s) - agriculture , fuel efficiency , production (economics) , environmental economics , energy consumption , consumption (sociology) , motor fuel , quality (philosophy) , agricultural productivity , natural resource economics , business , environmental science , computer science , risk analysis (engineering) , economics , engineering , automotive engineering , waste management , microeconomics , ecology , social science , philosophy , electrical engineering , epistemology , sociology , biology , gasoline
Modern agriculture is characterized by a continuous increase in production, which is inextricably linked with the growth of consumption of fuel and energy resources (FER). The consumption of motor fuel for agricultural enterprises is about 40% of the total consumption of fuel and energy resources. The use of motor fuel inevitably leads to emissions of pollutants. Predicting the negative impact on the environment depends on the quantity and quality of these emissions. The purpose of the research is: analysis of forecasting methods; selection and justification of the most attractive forecasting method; planning of measures to reduce emissions into the environment. The statistical method of forecasting, which uses the theory of multidimensional dynamic stochastic processes, is the most attractive. The development and implementation of measures developed taking into account the analysis of the forecast of the negative impact on the environment of vehicles of agricultural enterprises will reduce the negative impact on the environment by introducing energy-efficient technologies into the energy balance of the enterprise.