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Check the data.
Check data integrity, for example, the integral correlation data, the correct amount of data.
The amount of the source data is equal to the theoretical quantity of data and a washing out of the processing data.
Compare the data.
A large reduction is in the test cycle with the use of automated tool in data comparison, while it can improve the coverage of test data, and even up to 100% coverage, reduce the technical requirements for testers, and the staff freed from the single and complex work.

Building energy efficiency is the basis for achieving carbon reduction and it should develop into low-carbon building.
Low-carbon buildings (LCB) are buildings which are specifically engineered with Greenhouse Gas (GHG) reduction in mind.
In practice, energy-saving technologies will invariably result in the reduction of carbon emissions, while carbon reduction technologies do not necessarily lead to energy saving.
Data show that the construction of a building consumes the same amount of energy as that consumed in 6 years when the same building is in use.
WWF research data show saving 1 kWh of electricity equals to a reduction of 1 kg carbon dioxide emission; not using 10 pairs of disposable chopsticks, a reduction of 0.2 kg; not driving the car for one day, a reduction of 8.17 kg; hand-washing clothes instead of using a washing machine, a reduction of 0.3 kg.

Based on capacity planning data, we can estimate the annual generating capacity of states and municipalities, and thus can calculate quantitative indicators of the city's energy saving benefits, such as shown in Table 1.
Table 1 Analysis table of Yunnan planning wind farms’ energy conservation and emission reduction benefits Sequence Number City Planning installed [MW] Annual generation capacity of [100 million kW·h] Energy saving and emission reduction benefits [tons / year] Standard coal saving CO2 emissions reduction SO2 emissions reduction NOx emissions reduction Solid particles emission reduction 1 Kunming 4024.5 80.49 269.64 779.95 0.55 0.52 0.28 2 Qujing 4585.5 91.71 307.23 888.67 0.63 0.59 0.32 3 Yuxi 1693.5 33.87 113.46 328.2 0.23 0.22 0.12 4 Zhaotong 1141.5 22.83 76.48 221.22 0.16 0.15 0.08 5 Baoshan 1704 34.08 114.17 330.24 0.23 0.22 0.12 6 Chuxiong 3819 76.38 255.87 740.12 0.53 0.49 0.27 7 Honghe 2955 59.10 197.99 572.68 0.41 0.38 0.21 8 Wenshan 672 13.44 45.02 130.23 0.09 0.09 0.05 9 Pu'er 1591.5 31.83 106.63 308.43 0.22 0.20 0.11 10 Xishuangbanna 118.5 2.37 7.94 22.97 0.02 0.02 0.01 11 Dali 6216 124.32 416.47 1204.66 0.86 0.80 0.44 12 Dehong 126 2.52 8.44 24.42 0.02 0.02 0.01 13 Lijiang
1134 22.68 75.98 219.77 0.16 0.15 0.08 14 Nujiang 252 5.04 16.88 48.84 0.03 0.03 0.02 15 Diqing 276 5.52 18.49 53.49 0.04 0.04 0.02 16 Lincang 1351.5 27.03 90.55 261.92 0.19 0.17 0.09 Total 31660.5 633.21 2121.24 6135.81 4.36 4.05 2.22 Note: 1) Dates of Planning wind power installed capacity and the annual generation capacity are from the Energy Bureau of Yunnan Province: Yunnan wind power planning report (2011-2020); 2) The generating capacity of wind farms according to the number of 2000h; 3) Equivalent to the standard coal unified 335g/kW·h; 4) CO2,SO2,NOx and solid particles emission reduction according to 969g/kW·h, 0.688g/kW·h, 0.64g/kW·h, 0.35g/kW·h. [1,2] Put data =969g/kW·h, = 10EUR/t [3] and the Yunnan Province City annual generating capacity data into the formula (1): = 5.146 billion yuan RMB.
Emission reduction effect of the wind power development projects is outstanding.
Emission reduction contribution of wind power development projects mainly reflected in the zero emissions of carbon dioxide and other greenhouse gases.

Ammonia volatilization likely contribute to the reduction of the ammoniacal nitrogen content assessed.
This data indicate that 16 days of static pile composting with low aeration would be sufficient.
After 20 days of biological pretreatment, the organic matters were reduced from 45.6% to 38.2%, the moisture content reduction was observed from 42.4% to 35.2%, weight reduction was 15%, and volume reduction was reached 20% of the initial volume.
However, few data are available on the modifications of the nitrogen forms occurring during the short-term biological pretreatment.
This data indicate that 16 days of static pile composting with low aeration would be sufficient.

The former depends on detailed geological survey data, which is not easy to be satisfied in practice under the condition of lack of geological data.
Rock Mass in other two areas is still under loading conditions, so initial mechanical parameters will be used, while in the other two areas, which are usually provided by geological data.
Table 1 Relation between the variation of deformation modulus and reduction extent of stress Reduction extent of tress <30% 30-50% 50-80% 80-90% 90-100% Reduction extent of deformation modulus 1-7% 3-17% 8-31% 15-38% 29-61% Calculation of stress perpendicular to excavation face.
Fig.3 Stratum distribution situation Fig.4 Mechanical model of the section for calculation Initial mechanical parameters of slope rock mass provided by geological data are in table 2, in which is unit weight, E is deformation modulus, is Poisson's ratio, C is cohesion, is friction angle, and is tensile strength of rock mass.
Fig.5 Reduction extent of Fig.6 Reduction extent of Fig.7 Reduction extent of From figure 5, we can see that there is small stress decreased area after excavation when the first principal stress is singled out for calculation.

As shown in the survey data issued at the C40 Large Cities Climate Summit on May 19, 2009, 80% of global greenhouse gas emissions come from urban areas occupying only 2% of the earth's surface[1].
Data Source and Methodology As it is impossible directly obtain the original data of the survey report on the environmental awareness of all the cities in China, the relevant papers included in CNKI between 2007 and 2009 have been taken as the data source, and the urban residents have been selected as the research objects to carry out analysis based on the percentage data publicized in the survey report.
For example, the average scores in 2 relevant survey reports have been selected for data of Yunnan, Lanzhou and Dalian, and that of data in 3 relevant survey report have been selected as data for Shanghai, while that of data in 3 urban survey data in Hohhot, Baotou and Ordos has been adopted as the data source for Inner Mongolia Autonomous Region.
Such data can represent the current situations of the level of environmental awareness of urban residents in the eastern, central and western areas of China in recent 3 years to a certain extent [3-6].
All the data came from "China City Statistics Yearbook 2008", and data processing was finished by SPSS, while the principal component analysis was adopted for analysis [9].

In this paper, the author based on the calculated basic data to analyze on China's low carbon pilot provinces’s CO2 emission characteristics and use Kaya model to decompose the factors influencing the size and driving forces.
For lacking data of other cities set by national development and Reform Commission, they are not in this list.
As the present situation that CO2 emissions data is very lack in China, the author uses the data of China's energy consumption to calculate CO2 emissions of the inter-provincial department and the type of energy.
We use the energy consumption of various provinces and departments multiplying CO2 emission coefficient of various energy as the calculating method, the energy consumption data is from the ，the discharge coefficient is from Technology Policy Research Institute [3]which is from Japanese research achievements of science and Technology Department of science.
The CO2 emissions of Chongqing increase to 79 million tons, economic growth is the only increased emissions factors, which increases the capacity of 145 million tons, the other three factors are the reduction factors, energy saving factors with the reduction of 047 million tons, energy conversion emissions with the reduction of 17 million tons, and the driving force factors of population movement is very small, only 2 million tons.

Preparation and Electrochemical Properties of Pt/Mesoporous-C for Oxygen Reduction Reaction: Effect of Carbonation Temperature Chia-Ying Hsieh1, a, Yi-Shiuan Huang2, b, and Jing-Shan Do2, c, * 1Department of Chemical and Materials Engineering, Tunghai University, Taichung 407, Taiwan 2Department of Chemical and Materials Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan am19840115@yahoo.com.tw, bshane741108@gmail.com, cjsdo@ncut.edu.tw, *corresponding author Keywords: Mesoporous carbon; Pt/MC electrocatalyst; Mass activity; Specific activity; Oxygen reduction reaction.
The maximum mass activity (MA) and specific activity (SA) of the oxygen reduction reaction (ORR) on Pt/MC (P/Si = 0.5, Tc = 900 °C) are experimentally found to be 15.54 A g-1 and 18.05 mA cm-2, respectively.
Introduction Commercialization of the polymeric electrolyte membrane fuel cell (PEMFC) is generally limited by the sluggish kinetics of oxygen reduction reaction (ORR) on the cathode.
The difference of Pt loading between the design value (20.0%) and the experimental data were inferred to be: (i) uncompleted reduction of the platinum precursor; (ii) adsorption of organics on the carbon supports.

Based on the experimental data and literature as well as using the CFD tools, a model of light fuel oil combustion has been developed with an emphasis on nitric oxide formation.
The quantitative results obtained are comparable to the experimental data.
Considering the nitrogen-bearing fuel, the precursor to NO is HCN which is converted to NO (oxidation) or N2 (reduction) [16-18].
On the quantitative basis, the obtained results are comparable to the experimental data.
Gil, Influence of pressure on the rate of nitric oxide reduction by char, Combustion and Flame 126 (2001) 1602 – 1606

Therefore, it is considered that these results may provide an available reference data to improve effectively the quality of Zn anode when the anode may be applied in low conductivity solution mixed with fresh water and sea water.
Table 3 shows reduction weight ratio of five types of samples.
Fig. 9 shows comparison of reduction weight ratio of all samples in 0.8 % NaCl solution.
Comparison of reduction weight Ratio of five samples in 0.8% NaCl solution.
On the contrary, the highest value of reduction weight ratio was observed at the 2.6% sample.