Abstract

A three-level model for estimating greenhouse gas (GHG) emissions by mobile and stationary road transport facilities of a state or region, proposed in this article, takes account into GHG emissions from a vehicle fleet (mobile objects) and road transport infrastructure (network of car services, road network of various categories). Additionally, it has been developed the intellectual system which evaluates the reliability of the array of initial data, by increasing the range and adjusting (if necessary) the values of individual indicators, as the result we achieving the convergence of the calculating GHG emissions from motor vehicles according to the models of all three levels of assessment. This ensures verification of the obtained gross GHG emissions. Evaluation of greenhouse gas emissions using three-level model was carried out for St. Petersburg and the Leningrad Region (Russian Federation), they shown the possibility of reducing by 2030 by 3.2 ... 12.4% of gross GHG emissions by motor transport of the Russian Federation in comparison with 2015. For St. Petersburg and the Leningrad Region, both the reduction of gross GHG emissions by road transport (12.7% innovative scenario) and their growth (4.8% inertial scenario) are expected during the forecast period. At the same time, both for the St. Petersburg and the Leningrad Region and for the state as a whole, a significant reduction in gross GHG emissions by road transport is expected in the period after 2025 due to the intensive replacement of cars on oil fuel by electric vehicles and hybrids, changes in the transport behavior of the population, the development of public passenger transport and cycling, the introduction of autonomous vehicles, etc.

Highlights

  • Ensuring the transition of transport sector to a low-carbon model of development should be the key vector of modern transport policy, which is impossible without the creation of an effective system of accounting, monitoring and forecasting of gross greenhouse gas (GHG) emissions by individual types of transport and primarily by road transport, which, for example, in the Russian Federation accounts for 2/3 of gross GHG emissions by all types of transport [1]

  • Petersburg and the Leningrad Region, both the reduction of gross GHG emissions by road transport (12.7% innovative scenario) and their growth (4.8% inertial scenario) are expected during the forecast period

  • Petersburg and the Leningrad Region and for the state as a whole, a significant reduction in gross GHG emissions by road transport is expected in the period after 2025 due to the intensive replacement of cars on oil fuel by electric vehicles and hybrids, changes in the transport behavior of the population, the development of public passenger transport and cycling, the introduction of autonomous vehicles, etc

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Summary

Introduction

Ensuring the transition of transport sector to a low-carbon model of development should be the key vector of modern transport policy, which is impossible without the creation of an effective system of accounting, monitoring and forecasting of gross greenhouse gas (GHG) emissions by individual types of transport and primarily by road transport, which, for example, in the Russian Federation accounts for 2/3 of gross GHG emissions by all types of transport [1] Such a system for recording, monitoring and forecasting GHG emissions from a region or a state can be created using mathemat-ical modeling methods based on the implementation of the methodological principles of the United Nations International Panel on Climate Change (IPCC) [2], accounting for GHG emis-sions primarily from mobile vehicles (motor vehicles [3] or road traffic on the road network). Considering that part of the source data cannot be determined based on the results of statistical analysis or field measurements, but only by calculation, it is important to verify the results of estimat-ing GHG emissions, which can be performed using different methods [6] and [7]

Methodology for the forecasting of greenhouse gas emissions
Scenarios forecasting the initial data of the estimated model
Results and Discussion
Conclusion
Full Text
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