Decomposition Analysis and Trend Prediction of CO2 Emissions in China’s Transportation Industry

Abstract

China’s transportation industry has become one of the major industries with rapid growth in CO2 emissions, which has a significant impact in controlling the increase of CO2 emissions. Therefore, it is extremely necessary to use a hybrid trend extrapolation model to project the future carbon dioxide emissions of China. On account of the Intergovernmental Panel on Climate Change (IPCC) inventory method of carbon accounting, this paper applied the Logarithmic Mean Divisia Index (LMDI) model to study the factors affected by CO2 emissions. The affected factors are further subdivided into the scale of employees, per capita carrying capacity, transport intensity, average transportation distance, energy input and output structure, energy intensity and industrial structure. The results are as follows: (1) Per capita carrying capacity is the most important factor to promote the growth of CO2 emissions, while industrial structure is the main reason to inhibit the growth of CO2 emissions; (2) the expansion of the number of employees has played a positive role in the growth of CO2 emissions and the organization and technology management of the transportation industry should be strengthened; (3) comprehensive transportation development strategy can make the transportation intensity effect effectively reduce CO2 emissions; (4) the CO2 emissions of the transportation industry will continue to increase during 2018–2025, with a cumulative value of about 336.11 million tons. The purpose of this study is to provide scientific guidance for the government’s emission reduction measures in the transportation industry. In addition, there are still some deficiencies in the study of its influencing factors in this paper and further improvements are necessary for the subsequent research expansion.

Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios

“…A forecast of transportation CO 2 emissions can help us understand the development trend and support the policy making of carbon emission reduction. Researchers have forecasted China's transportation CO 2 emissions across the country [12][13][14] for provinces such as Jiangsu [15,16], Hubei [17][18][19], Hebei [20], Shandon [21], Shaanxi [22], Qinghai [23], Jilin [24], and Hainan [25] and cities such as Beijing [26,27] and Tianjin [28] using the Kaya model [13], the STIRFDT(Stochastic Impacts by Regression on Population, Affluence, and Technology) model [1,17,18,23,[29][30][31], the LEAP (Long-range Energy Alternatives Planning System) method [32], the linear regression method [14], the gray model [20,22,33,34], the LMDI (Logarithmic Mean Divisia Index) method [35,36], the machine-learning method [15,27,37], the system dynamics method [28,[38][39][40], and so on. These methods are often used in combination with a scenario analysis to make predictions [1,13,[16...…”

Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model

“…Clarifying the influencing factors of transportation CO 2 emissions is the basis for establishing prediction models. Researchers have explored the influencing factors of transportation CO 2 emissions from different perspectives, including the influencing factors related to energy and industry, such as population size, economic growth [11], energy structure, energy efficiency [12], industrial structure [13], energy intensity [14,15], transportation intensity [4,16], urbanization level, technological innovation level [17], degree of trade openness [18], and fixed asset investment; the factors related to the transportation sector, such as transportation infrastructure investment [19], transportation mode [20], transportation industry added value [21], civilian car ownership [22], freight turnover [23], and passenger turnover [24]; and the factors related to transportation infrastructure, such as road mileage [25], public transportation [26,27], city size, urban road density, and private car ownership [28,29].…”

An Inquiry into the Characteristics of Carbon Emissions in Inter-Provincial Transportation in China: Aiming to Typological Strategies for Carbon Reduction in Regional Transportation

“…Existing studies have explored the macro-level influencing factors of TCO 2 characteristics, including economic development level, population size, transportation energy intensity, transportation energy structure, transportation intensity, and industrial structure [5,[24][25][26]. Additionally, studies have examined the impacts of transportation infrastructure development, such as urbanization rate, fixed asset investment in the transportation industry [27,28], length of road network [29][30][31], level of public transportation development [32,33], per capita private car ownership, passenger and freight turnover [34,35], average transportation distance [36], logistics scale, and express delivery industry development [37,38]. Furthermore, the impacts of transportation technology level and new energy industry planning have been investigated, such as R&D investment [39,40], level of digital innovation [41], and new energy vehicle industry [42], etc.…”