Logarithmic Mean Divisia Index Decomposition of CO2 Emissions from Urban Passenger Transport: An Empirical Study of Global Cities from 1960–2001

Abstract

The urban transport sector has become one of the major contributors to global CO2 emissions. This paper investigates the driving forces of changes in CO2 emissions from the passenger transport sectors in different cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO2 emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO2 emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO2 emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some effective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita; (ii) fuel economy policies and standards formulated when there are oil crisis are effective ways to suppress the increase of CO2 emissions, and these changes should not be abandoned when oil prices fall; and (iii) cities with high population densities should focus on the development of public and non-motorized transport.

Logarithmic Mean Divisia Index Decomposition Based on Kaya Identity of GHG Emissions from Agricultural Sector in Baltic States

“…Index decomposition analysis (IDA) is an analytical tool that is used to measure different driving factors and their environmental side effects [40]. This method allows scientists to break down the aggregate indicator into a combination of several factors and to determine the influence of a single factor on the aggregate indicator [41]. In other words, the IDA method allows us to measure the impact of the main factors influencing changes over time in the GHG emissions produced by the agricultural sector.…”

“…The IDA method can be divided into the Divisia IDA method and the Laspeyres IDA method [39]. Although both of these methods are used in research, the Divisia index method is superior to the Laspeyres index method because in the process of decomposition, there are no residual terms [41]; it considers the change in factors over time [42]. Ang and Liu (2001) [43] improved the Divisia index method and proposed a logarithmic mean Divisia index (LMDI) with good decomposition and aggregation.…”

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“…Index decomposition analysis (IDA) is a comprehensive mechanism that allows to identify the main drivers of change in environmental performance indicators [11]. The conceptual framework of IDA is based on the decomposition of the study phenomenon, breaking down the element of the research study into several factors to examine their influence on the outcome [12].…”

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“…They found that the main factor behind carbon emissions growth is the transport sector. Tu et al ( 2019 ) employed LMDI decomposition to investigate the changes in carbon emissions of the transport sector from 1960 to 2001 in different cities. They suggested some effective strategies to reduce carbon emissions, such as rational land use, fuel economy policies, and the development of public transport.…”