Using flood disaster historical data and social economic data in China for the period 1984-2012, the characteristics of disaster exposure were analyzed from the scope of disaster exposure, population exposure, economy exposure and crop exposure, and those of disaster vulnerability were analyzed from two aspects of population vulnerability and economy vulnerability. The results show that from 1984 to 2012, the average scope of disaster exposure, population exposure, economy exposure and crop exposure was respectively 93700 km², 126 person/km², RMB 1.49 million/km² and 153 million hm². The level of exposure significantly increased overall, the areas with the highest exposure were mainly distributed in the coastal provinces (municipalities). Significant increase trend was found for population vulnerability, but economy vulnerability had a gradually-decreasing trend. The highest level of disaster vulnerability were mainly distributed in Hunan, Anhui, Chongqing, Jiangxi and Hubei provinces (municipalities) along the coast in the middle reach of Yangtze River, and Shanghai, Beijing, Tianjin are areas with the lowest level of disaster vulnerability. High vulnerable areas of flood disaster are also the most prone areas of flood disaster in China, which no doubt become high risk areas of flood disaster. Therefore, as far as the design and implementation of strategies and policies for adaptation and disaster risk management are concerned, more attention should be paid to these regions to reduce the vulnerability and exposure, and to improve the resilience towards the adverse effects of flood disaster, so as to promote the sustainable development of society and economy.

In order to better understand the chemical components and their variations of background aerosols in the North China Plain, sixty-four PM10 samples were collected on the top of Mount Tai from June 2010 to July 2011. The mass concentrations and seasonal variations of PM10 as well as its nine water soluble ions, organic carbon (OC) and elemental carbon (EC) have been analyzed. The correlation analysis of various chemical components has also been performed. The annual average mass concentration of PM10 is 68.4 mg/m³, and inorganic salts accounts for 64.8% in PM10, while carbon aerosol accounts for 17.4%. The mass concentration of inorganic salt increases gradually from spring, reaches its peak in summer, then decreases in autumn and comes to the lowest in winter. The mass concentration of OC increases from spring to autumn and reaches the minima in winter. Similar pattern has been found in the mass concentration of EC; however, the EC concentrations of summer and autumn are similar. The ratio of secondary organic carbon (SOC) to OC is above 50% for all seasons with an annual average of 58.5%. Back trajectory analysis shows that when Mount Tai is mainly influenced by air masses from the southern area and megacities, mass concentrations of PM10 and its components are high; while it is mainly influenced by air masses from Northwest China through long distance transportation, the mass concentrations of PM10 and its components are much lower.

Based on the simulations using the University of Victoria Earth System Climate Model under the scenario of RCP8.5, the responses of ocean carbon cycle to increasing atmospheric CO₂ and climate change from year 1800 to 2500 were analyzed. Relative to the simulation case with no climate change, the simulation with climate sensitivity 3.0 K shows that in 2100, due to the increase of atmospheric CO₂ concentrations simulated sea surface temperature increases by 2.7 K, the intensity of the North Atlantic deep water formation reduces by 4.5 Sv, and oceanic uptake of anthropogenic CO₂ decreases by 0.8 Pg C. As for ocean column inventory of anthropogenic CO₂, climate change is found to have a large effect in the North Atlantic. During 1800-2500, compared to the simulation with no climate change, climate change causes a reduction of total anthropogenic CO₂ column inventory in the entire ocean and North Atlantic by 23.1% and 32.0%, respectively. A set of simulations with climate sensitivity varying from 0.5 K to 4.5 K show that with a greater climate sensitivity climate change would have a larger effect in reducing the ocean’s ability to absorb CO₂ from the atmosphere.

Based on the 1 km CO₂ emission gridded dataset, the CO₂ emissions of Shanghai’s four urban boundaries, namely city administrative boundary (UB1), city district boundary (UB2), city built-up area (UB3) and urban proper (UB4) respectively, were analyzed and compared. The spatial characteristics of CO₂ emissions of UB1 showed three gradient distribution patterns with city center as the hotspot and emissions of grids gradually decreasing outward. There was spatially clustering phenomenon in CO₂ emissions in UB1, and emissions of some local areas may have significant positive effect on its surrounding area, which indicated by spatial autocorrelation analysis. The UB4 is the appropriate boundary for city of Shanghai. The CO₂ emissions in UB4 totaled to 189 million t, and per capita emissions reached 12.04 t in 2007. The emissions of UB4 accounted for 75.40% of the emissions of UB1. The per capita emissions of UB1 were 12% higher than that of UB4. The industrial area and urban area were spatially coincided in a high degree in Shanghai, which led to high emission sources concentrated in UB4. Emissions of top one grid accounted roughly for 10%-20% of total emissions in 3 correspondent urban boundaries. The emission sum of top 10 and top 100 grids accounted for more than 60% and 80% respectively, of the total emissions in 3 correspondent urban boundaries.

Three total column dry-air mole fractions of CO₂ (XCO₂) products from satellite retrievals, including SCIAMACHY, NIES-GOSAT, and ACOS-GOSAT, in the Northern Hemisphere were validated by ground data from the Total Carbon Column Observing Network (TCCON). The results show that the satellite data show the same seasonal fluctuations with TCCON, in general with maximum in April or May and minimum in August or September. The three products all underestimate the XCO₂. The accuracy of the ACOS-GOSAT product is almost the same as the NIES-GOSAT product, and their mean standard deviations are 2.26×10^-6 and 2.27×10^-6 , respectively. The accuracy of the SCIAMACHY product is slightly lower, with mean standard deviation of 2.91×10^-6.

Land use change and forestry (LUCF) is one of the key sectors to report national greenhouse gases (GHG) inventory as required by the United Nations Framework Convention on Climate Change (UNFCCC). With the aim to further implement the GHG emissions reduction target assigned to all provinces in China, it is an important basic work to estimate the GHG emissions by sources and removal by sinks, and their present situations and distributions at provincial level, that is, to prepare provincial GHG inventory in LUCF sector. The methodology of compiling provincial GHG inventory in LUCF sector is developed to estimate anthropogenic GHG emissions by sources and removal by sinks from LUCF activities at provincial level in China, which mainly includes carbon stock changes in forest and other woody biomass, and GHG emissions caused by forest conversion. The methodology is in compliance with IPCC guidelines on national GHG inventory and incorporated with actual specialty of LUCF activities in China, especially fully considers the availability and reliability of key activity data at provincial level. The methodology also supplies provincial specific defaults of key emission factors. The methodology makes it scientific and operable to compile LUCF GHG inventory at provincial level in China.

With the aim of providing a supplement to 2006 IPCC Guidelines for National Greenhouse Gas Inventories to fill in the gaps within it on the methodological guidance for the inventory of anthropogenic emissions and removals of greenhouse gases (GHG) from wetlands, 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands was published in February 2014. On the basis of effect of human activity and deliberation again on the definition of wetlands, the supplement provided methods for the estimation of GHG emissions and removals from wetlands under drainage and rewetting. The supplement also presented a methodology of estimation of GHG emissions and removals from coastal wetlands and constructed wetlands for wastewater treatment by human activities. The publication of the supplement provides a comprehensive methodological guidance for inventory compilers to estimate the GHG emissions and removals from wetlands under human’s activities. Nevertheless, by the limitation from the scientific development and academic literature availability, some methodological guidance for the estimation of GHG emissions from wetlands, such as the loss of particulate organic carbon from drainage, emission of organic carbon from external ecosystems to the wetlands by rewetting, is still needed to be further developed.

In order to measure the fairness of the right of aviation carbon emissions, the carbon Lorentz curve and Gini coefficient were constructed on the basis of the international aviation history cumulative CO₂ emissions per capita. This way was borrowed from the research idea of fair income distribution. The results show that there is a huge unfairness about international aviation carbon emissions in history, and with the accumulative starting year delaying, the unfairness is partially hidden. Fair allocation of carbon emissions is the key to build international aviation global emissions reduction mechanism. A fair method is proposed in this paper to allocate the aviation carbon emission rights on the basis of responsibility-capacity index. Taking the goal of “carbon-neutral growth by 2020” as an example, the carbon emission reductions of different states in 2021 were calculated with the use of the proposed method.

The EU 2030 Policy Framework for Climate and Energy (hereinafter to be referred as the framework) proposes that EU will reduce 40% of GHG emissions in 2030 at the level of 1990 and will raise the portion of renewable energy in primary energy consumption up to 27%. Although it has not been ratified yet, the framework has been focused due to its possibility to become a basis for domestic legislations for its participation in the 2015 international climate change negotiation. By identifying and analysing key elements, the framework shows that its 2030 goal, in terms of targets of emissions reduction and renewable energy, is more positive than that in 2020. In terms of achievement, the target of renewable energy is more difficult than that of emissions reduction. Because of inclusion of Eastern European countries, on the one hand, EU receives the surplus of emissions allowance, which helps EU achieve the target of emissions reduction, on the other hand, a growing disparity among member states of EU constrains from positive environmental policies of EU and reduces the possibility of a great extent to which EU adjusts the target of emissions reduction. The emissions reduction target in the framework is likely to influence the projection of demand of post-2020 international carbon market. The future international carbon market requires not only an ambitious target set by developed countries outside EU, but also a higher target of emissions reduction for EU. Some ideas of the framework such as taking climate change as the driving force of economic development rather than resistance, various approaches to fulfil different type of goals, the transparency of setting goals, and a full consideration of disparities among member states, etc., are worth learning for China.