Uncovering CO2 Emissions Patterns from China-Oriented International Maritime Transport: Decomposition and Decoupling Analysis

The nonlinear effect of land freight structure on carbon emission intensity: new evidence from road and rail freight in China

The extensive literature has debated the varying effects of factors on carbon dioxide (CO₂) emissions. However, it has paid little attention to land freight structure (FS), including road and rail freight share, which may have different effects on CO₂ emissions. Based on the data from 6 eastern provinces in China during 2005-2019, the panel threshold model is used to explore the dynamic influence mechanism of road and rail freight share on transport carbon emission intensity (CE), respectively. The results show different nonlinear relationships between the share of road and rail freight and transport carbon emission intensity. First, the effect of road freight share on carbon emission intensity is all positive across different stages of trade openness, while such effect goes through a process of increasing and then decreasing with the level of trade openness improving. Second, the driving effect of rail freight share on carbon emission intensity exhibits a "negative-positive-negative" feature as the level of trade openness increases. Third, trade openness generates a double-threshold effect on carbon emission intensity. The differentiated nonlinear effects provide significant evidence of the modal shift from road to rail freight, which would be effective to alleviate transport CO₂ emissions.

Impacts of regional development on emissions in China's transport sector

The CO₂ emissions in China's transport sector increased from 349.00 Mt in 2005 to 723.87 Mt in 2017. Thus, a number of climate change policies are being implemented to adjust regional structure and to decrease the emissions in China's transport sector at the regional level. However, few studies explored the impact of changes in regional structure (that is, measured regional share of the added value of transport sector) on emissions in China's transport sector. Therefore, based on the Kaya identity and LMDI analysis, we decompose 8 factors (including carbon intensity, energy structure, energy intensity, turnover intensity, transport intensity, regional structure, per-capita traffic activity, and population size) to analyze the driving factors of emissions in China's transport sector. The period 1997-2017 is divided into four phases according to the growth rate of emissions. The results show that regional structure increased CO₂ emissions in China's transport sector between 2013 and 2017. The fast transport development in the Southwest region, reflected by the increase in the share of total transport value added, resulted in emissions growth during 2013-2017. Moreover, the change in the growth rate of the regional transport sector's value added is positively correlated with the change in the regional share of value added, which is positively correlated with the change in regional emissions.

Bi-Objective Optimization of Vessel Speed and Route for Sustainable Coastal Shipping under the Regulations of Emission Control Areas

To comply with the regulations of emission control areas (ECAs), most operators have to switch to low-sulfur fuels inside the ECAs. Besides, a low-carbon objective is essential for long-term environmental protection; thus, is regarded as important as making profit. Therefore, the operators start making speed and route decisions under the two objectives of minimizing carbon emissions and maximizing profit. Drawing on existing methods, this paper formulates the profit and carbon emissions in sustainable coastal shipping, investigates the speed and route principles, and determines the best tradeoff between profit and carbon emissions. It is found that vessel speed should be set between emissions-optimum speed and profit-optimum speed, and the route must be selected in light of the speed decision. Next, the optimal choices of speed and route were examined under different scenarios and vessel types. The results show that the operation measures and objectives depend greatly on fuel price, vessel load, and vessel parameters. The operator should speed up the vessel if he/she wants to make more profit or if the scenario is favorable for profit making; e.g., low fuel price and high vessel load (LFHL). Large vessels should pursue more profit under LFHL conditions, without having to sail further outside the ECA. But this rule does not apply to small vessels. In addition, the operator should slow down the vessel inside the ECA and sail further, outside the ECA, with the growth in the price spread between marine gas oil (MGO) and heavy fuel oil (HFO), especially at a low HFO price. The research findings help operators to design operational measures that best suit the limit on sulfur content in fuel and the situation of the shipping market.

Analysis of the Agricultural Economy and Agricultural Pollution Using the Decoupling Index in Chengdu, China

The development of the agricultural economy has brought the agricultural pollution, and agricultural pollution has restrained the growth of the agricultural economy and endangered public health. Since the Economic Reform opened up, China has promulgated many policies that have benefited farmers, developed the agricultural economy, and improved agricultural material levels. However, the growth in the agricultural economy has also resulted in significant environmental pollution from pesticides, agricultural fertilizers and agricultural plastic film. Agricultural pollution can affect public health, therefore it is necessary to study the relationship between agricultural economy and agricultural pollution. In this paper, the relationship between the agricultural economy and agricultural pollution is studied by using decoupling index method, taking Chengdu, Sichuan Province, China as an example, and verified by the data of 2008–2017 years of Chengdu. It was found that the decoupling index for pesticide use and per capita gross agricultural production showed “strong decoupling” ideal states in six years. From 2009–2017, the value of it increased from 0.065743–1.597385, and its decoupling state gradually transited to stable strong decoupling. The chemical fertilizer use was found to have a “strong decoupling” ideal state in five years. The value of it decreased from 1.028854–(−0.512702), while its state gradually changed to strong decoupling. However, agricultural plastic film only has one “strong decoupling” state with its value of decoupling index changed from −0.380824–(−0.250250). The increased use of plastic film has resulted in increased agricultural pollution. The research results show that growing with the development of Chengdu’s agricultural economy, the use of pesticides and fertilizers has been decreasing. Chengdu’s agricultural economy and agricultural pollution were found to have a healthy and coordinated development. The development of agricultural economy has not caused much damage to the agricultural environment. In addition, with the decline of agricultural pollution, public health problems will also be reduced.

What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis

The rapid growth of household electricity consumption is threatening the sustainable development of China’s economy and environment because of its impacts on the operation efficiency of the electric power system. To recognize the driving factors of the consumption growth and offer policy implications, based on the consumption-related data of 2015 and 2016, this research used the rank sum ratio (RSR) method to divide China’s 30 provinces into 5 groups and a logarithmic mean Divisia index (LMDI) algorithm to decompose the composition growth of each group into the quantitative contribution of each driving factor. The following conclusions were drawn from the empirical analysis. (1) The Yangtze basin is the most vigorous region of consumption growth and should be principally monitored. (2) Climate conditions have a remarkable impact on consumption growth and should be a key consideration when making differentiated household electricity policies. (3) The rebound effect has already appeared in a few of the most developed regions. Electricity price is an effective measure in dealing with this effect. (4) The improvement of the income level is the most important driving factor for consumption growth. (5) For provinces with high growth vitality, the change in the burden level of electricity expenditure prompts consumption growth. However, for provinces with low growth vitality, the situations are opposite. (6) The impacts of electricity price and population on consumption growth are negligible.