Provincial carbon emissions research is necessary for China to realize emissions reduction targets. Two-level decomposition model based on the Kaya identity was applied to uncover the main driving forces for the energy related carbon emissions in Shandong province from 1995 to 2011, an important energy base in China. Coal consumption is still the biggest contributor to the increased carbon emissions in Shandong. Decomposition results show that the affluence effect is the most important contributors to the carbon emissions increments. The energy intensity effect is the dominant factor in curbing carbon emissions. The emission coefficient effect plays an important negative but relatively minor effect on carbon emissions. Based on the local realities, a series of environment-friendly mitigation policies are raised by fully considering all of these influencing factors. Sustainable mitigation policies will pay more attention to the low-carbon economic development along with the significant energy intensity reduction in Shangdong province.

Carbon emissions research based on regional perspective is necessary and helpful for China to achieve its reduction targets. This research aims at analyzing the energy-related carbon emissions and finding out the most important driving forces for the carbon emissions increments in Guangdong province. LMDI (Logarithmic Mean Divisia Index) method based on the extended Kaya identity has been used to explore the main driving factors for energy-related carbon emissions in Guangdong province annually between 1990 and 2014. Research results show that the impacts and influences of various factors on carbon emissions are different in the different development stages. Economic growth effect and population size effect are the two most important driving factors for the increased carbon emissions. Energy intensity effect played the dominant role in curbing carbon emissions. Energy structure effect and technical progress effect had different but relatively minor effects on carbon emissions during the five different development stages.

Regional carbon dioxide emissions study is necessary for China to realize the emissions mitigation. An environmental input–output structural decomposition analysis (IO-SDA) has been conducted in order to uncover the driving forces for the increment in energy-related carbon dioxide emissions in Xinjiang from both production and final demands perspectives from 1997 to 2007. According to our research outcomes, emissions increase can be illustrated as a competition between consumption growth (per capita GDP) and efficiency improvement (carbon emission intensity). Consumption growth have caused an increase of 109.98 Mt carbon dioxide emissions during 1997 to 2007, and efficiency improvement have caused a 97.03 Mt decrease during the same period. Per capita GDP is the most important driver for the rapid emission growth, while carbon emission intensity is the significant contributor to offset these increments. In addition, production structure changes performed as a new major driver for the steep rise in carbon dioxide emissions in recent years (2002–2007), indicating that the rapid emission growth in Xinjiang is the result of structural changes in the economy making it more carbon-intensive. From the viewpoint of final demands, fixed capital formation contributed the highest carbon dioxide emission, followed by inter-provincial export and urban residential consumption; while inter-provincial imports had the biggest contributions to offset emission increments. Based on our analysis results, Xinjiang may face great challenges to curb carbon dioxide emissions in the near future. However, several concrete mitigation measures have been further discussed and then raised by considering the regional realities, aiming to harmonize regional development and carbon dioxide emissions reduction.

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10^th five-year plan period (2003–2005), the 11^th five-year plan period (2005–2010), and the 12^th five-year plan period (2010–2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.

Resource endowment and resource carrying capacity (RCC) are the most important cornerstones of regional sustainable development. By applying a newly established RCC method, namely, the relative resources carrying capacity (RRCC) method, we evaluated the RCC in the Tarim River basin (TRB) in arid China in Xinjiang during 2000-2011. Research results show that the relative resources carrying capacity can be effectively served as indicators of regional resources sustainable development status. Taking Xinjiang as the reference region, the TRB can be characterized as an area with an overloaded population and surplus economy during the whole research period, where land resources are relative advantage resources carrying the regional population growth and economic development and water resources are relative disadvantage resources restricting rapid regional development. Taking TRB as the reference region, 42 counties and cities in TRB performed significant temporal and spatial disparities according to the RRCC based on population and economic perspectives. Based on the spatial-temporal evolution of RRCC, 42 counties and cities in TRB were classified into four matching modes (A, B, C, and D). The spatial-temporal evolution characteristics and mechanisms about the 42 prefectures and cities were examined, and the suitable development strategies for every different mode were recommended.