• Energy Research

Abstract The development of the regional economy in China is unbalanced. Interregional "carbon leakage" and "carbon transfer" have a significant impact on the realization of carbon emission mitigation targets and the allocation of responsibility. Using China's interregional input-output tables and the corresponding carbon emission data for 2007 and 2010, this paper proposes an interregional Ghosh input-output model and estimates the amount of interprovincial carbon emission transfer from the supply-side perspective. We find that the main direction of supply-side carbon emission transfer is from resource-intensive provinces in the central and western regions to developed provinces in the eastern coastal regions. Further analysis shows that the transfer is mainly concentrated in the production and distribution of electricity and heat, nonmetallic mineral products, and basic metals, whose carbon emissions account for approximately 70% of all sectors’ carbon emissions. The differences between the supply side and the demand side of China's provinces are mainly reflected in the transfer out of carbon emissions.

Abstract The striking disparities among China’s provinces suggest the need for regionalized carbon emission intensity mitigation measures. This study extends the aggregate embodied energy/emission intensity measures proposed by Su and Ang (2017) to a national multi-region setting, defines the aggregate embodied CO2 emission intensity (AECI) indicators at the aggregate, provincial, and final demand category levels from the demand perspective, and adopts multiplicative structural decomposition analysis (SDA) to identify the driving factors of the historical changes over the 2007–2012 period in China. On the basis of China’s latest multiregion input-output tables, we find that (a) the AECIs show heterogeneities across the provinces and final demand categories, with the AECIs of inland provinces higher than those of coastal provinces and the AECIs of investment and export higher than those of consumption; (b) the generally downward trends of the AECIs hold at different levels, and the improvements in carbon emission performances are more pronounced in inland provinces, presenting a convergence toward the levels of developed coastal provinces; (c) according to the multiplicative SDA results, the input structure effect and the emission intensity effect generally contribute to the decline of AECIs. By providing a deeper understanding of carbon emission performance and its driving factors at the subnational level, our study finally proposes some policy recommendations.

Abstract China CO2 allowance (CHA) allocation for emitters is one of the pivotal issues to build the effective national carbon market. In this study, we propose a multi-objective decision approach, incorporating the principles of fairness, efficiency, and feasibility, to allocate the CHAs to emitters from the industry perspective. Taking Guangdong, China as an example, we employ the proposed approach for allocating the CHAs to six major industries of petrochemical, chemical, cement, steel, nonferrous metals, and electricity power by 2030. The empirical results show that there are significant conflicts between principles. The proposed approach can not only effectively eliminate the defects of single-object models to make the allocation results more reasonable and acceptable, but also achieve optimal allocation options under various decision preferences. The power industry has the highest CHA and petrochemical industry has the lowest one, and petrochemical, chemical, and steel industries have the greatest potential to reduce carbon intensity. Single-factor sensitivity analysis shows that the CHA/104 Yuan added value (TY) of the industry with lower emissions is more sensitive to the changes of physical capital stock but received fewer impacts from the changes of emissions cap for six industries.

ABSTRACTThe paper estimated the balance of emissions embodied in bilateral trade and the pollution terms of trade between China and six major world economies, including USA, Japan, and others, from 1995 to 2009, and then discussed the factors affecting them using the Structural Decomposition Analysis method. We find that, with the exception of Taiwan, the balances of the haze pollutants emissions embodied in bilateral trades were negative between China and the each of the rest five, and this was mainly resulted from the China export scale effect and intermediate input structural effects. We also find that China has become the “Pollution Refuge” for the economies like USA and Japan.

Abstract To reduce carbon dioxide (CO2) emissions attributed widely to human activities, previous studies have paid great attention to the relationships between socioeconomic development, urban forms and CO2 emissions in cities, and provided relevant emission mitigation policies through the effective urban spatial planning. However, whether and how different features of urban forms (such as compactness) affecting the levels of CO2 emissions is still debatable, specifically considering the different development levels of the cities. Therefore, this study is to synthetically explore how socioeconomic factors and urban forms work together to affect CO2 emissions with the consideration of differences in development levels of five city tiers in China. First, CO2 emissions in each city were derived from provincial energy statistics, radiance-calibrated nighttime light imageries, and population distribution data based on a disaggregating model. Then, a set of variables representing socioeconomic factors and urban forms were acquired from the city statistics and land use data, respectively. After obtaining the balanced dataset of these five city tiers from 1995 to 2015, the panel data analysis was finally applied to evaluate the consequences of socioeconomic factors and urban forms on CO2 emissions under different development stages. The estimation results show that the economic development, population growth, and urban land expansion are important factors that accelerating CO2 emissions in all the city tiers. Besides, irregular or fragmented structures of urban land use could result in more CO2 emissions due to the increase in potential transportation requirements in all the city tiers. Notably, an increasing concentrated pattern in the urban core is found to increase CO2 emissions in the tier-one cities, but to promote the reduction of CO2 emissions in other four city tiers. The urban spatial development with a compact and multiple-nuclei pattern is suggested to be closely linked with a lower level of CO2 emissions. Such results highlight the importance of a city's development level for decision-making involving the mitigation of CO2 emissions, and provide scientific support for building a low-carbon city from the perspective of both socioeconomic development and urban spatial planning.

The exploration and modeling of the drivers of CO2 emissions can help make effective CO2 emission reduction policies. In this study, we examine the drivers of energy consumption-related CO2 emissions in China during 1978–2014 from a multiscale perspective. Firstly, we use the multivariate empirical mode decomposition model to simultaneously decompose the CO2 emissions and 17 drivers into several groups of intrinsic mode functions and one group of residues at different timescales. Secondly, we employ the stepwise regression analysis to explore and model the key drivers of CO2 emissions at different timescales without multicollinearity. The empirical results show that China’s CO2 emissions have obvious timescales of 6.17 years, 9.25 years, 18.5 years, 37.0 years, and long-term trend. At the short-term timescales, fuel structure and economic structure have significant impacts on CO2 emissions. At the medium-term timescales, urban population and fuel structure are the major contributors to CO2 emissions. At the long-term timescale, only per capita GDP has a positive effect on CO2 emissions. Finally, we propose the policy implications at the short, medium, and long timescales.

Against the background of global warming, China has vowed to meet a series of carbon emissions reduction targets and plans to launch a national carbon emissions rights trading market by 2017. Therefore, from the provincial value chain perspective, using input-output tables from China in 2002, 2007, and 2010, this study constructs models to calculate the CO2 emissions responsibility of each province under the production, consumption, and value capture principles, respectively. Empirical results indicate that Shandong, Hebei, Jiangsu, Guangdong, and Henan bear the most responsibility for CO2 emissions under the three principles in China, while Hainan and Qinghai have the least responsibility. However, there is a great difference in the proportion of carbon emissions responsibility for each province during the same period under different principles or different periods under the same principle. For consumption-oriented areas such as Beijing, Tianjin, Zhejiang, Shanghai, and Guangdong, the production principle is more favorable, and the consumption principle is more beneficial for production-oriented provinces such as Hebei, Henan, Liaoning, Shanxi, Inner Mongolia, and Shaanxi. However, the value capture principle strikes a compromise of the CO2 emissions responsibility of each province between the production and consumption principles, and it shares the CO2 emissions responsibility based on the actual value captured by each province in the provincial value chain. The value capture principle is conducive to the fair and reasonable division of CO2 emissions rights of each province by sectors, as well as the construction of a standardized carbon emissions rights trading market.

Abstract This paper expounds the influence of transport infrastructure on environment as spatial agglomerative, economic growth, innovation and technology diffusion effects. Within the STIRPAT model, we use a spatial Durbin model to estimate the impact of transport infrastructure on the environment in 281 Chinese cities during 2003-2013. The results show that transport infrastructure, technical progress, and energy intensity have negative direct effects on urban environment. Additionally, we find an inverted U-shaped curve relationship between GDP per capita and urban environment. As spatial effect, transport infrastructure has negative impacts, while technical progress has a positive effect.

Abstract Against the background of global warming, China faces the dual pressures of economic structural transformation and carbon emission reduction. While promoting economic development, the development and construction of transportation infrastructure has contributed to urban carbon emissions. Using an improved STIRPAT model, we examine panel data for 283 cities between 2003 and 2013 to explore the effects of transportation infrastructure on urban carbon emissions. The results show that transportation infrastructure increases urban carbon emissions and intensity. In addition, while the population scale effect of transportation infrastructure is conducive to decreasing carbon emissions, the economic growth and technological innovation effects of transportation infrastructure increase carbon emissions. Results also demonstrate that in large and medium-scale cities, construction of transportation infrastructure increases carbon emissions. In small cities, this relationship is not significant. Robustness tests support all findings. These results indicate that the effective development of carbon-abatement policies requires an examination of the effects of transportation infrastructure.