Criteria for assessing carbon emissions peaks at provincial level in China

China has pledged to peak carbon emissions by 2030 and neutralize emissions by 2060.There is an urgent need to develop a comprehensive and reliable methodology to judge whether a region has reached its carbon emissions peak(CEP),as well as to schedule and prioritize mitigation activities for different regions.In this study,we developed an approach for identifying the CEP status of 30 provincial areas in China,considering both the carbon emissions trends and the main socioeconomic factors that influence these trends.According to the results of the Mann-Kendall(MK)tests,changes in carbon emissions for the 30 provincial areas can be grouped inlo four clusters:those with significant reductions,marginal reductions,marginal increases,and significant increases.Then,total energy consumption(TEC),the proportion of coal consumption(PCC),the proportion of the urban population(PUP),the proportion of secondary industry(PASP),and per capita GDP(PGDP)were further identified as the main factors influencing carbon emissions,by applying Redundancy analysis(RDA)and Monte Carlo permutation tests.To balance efficacy with fairness,we assigned scores from 1 to 4 to trends in carbon emissions,and the Group Analysis results of the main influencing factors above except for TEC;for TEC,main basis is the relevant assessment results.And finally,according to the actual condition of total scores,provincial areas were assigned to the first,second,third and fourth stage of progress toward CEP,using the method of Natural Breaks(Jenks).Based on the method,differentiated plans should be adopted from the perspective of fair development and emissions reduction efficiency,in accordance with the basic principles of Doing the Best within Capacity and Common but Differentiated Responsibilities.This classification method can also be adopted by other developing countries which have not yet achieved CEP.

Impacts of Climate Change on Rice Yield in China From 1961 to 2010 Based on Provincial Data

The impacts of climate change on rice yield in China from 1961 to 2010 were studied in this paper, based on the provincial data, in order to develop scientiifc countermeasures. The results indicated that increase of average temperature improved single cropping rice production on national level by up to 11%relative to the average over the study period, however, it resulted in an overall loss of double cropping rice by up to 1.9%. The decrease of diurnal temperature range （DTR） in the major producing regions caused the decrease by up to 3.0%for single cropping rice production and 2.0%for double cropping rice production. Moreover, the contribution of precipitation change reached about 6.2%for single cropping rice production, but no signiifcant effect for double cropping rice production in recent 50 years.

Extraction of Planting Information of Winter Wheat in a Province Based on GF-1/WFV Images

In order to explore the adaptability of domestic high-resolution GF-1 satellite images in the extraction of planting information of crops especially in a province, based on the 16-meter remote sensing images of a multi-spectral wide-spectrum camera （WFV） carried by the GF-1 satellite as well as land use type and field survey data of Shandong Province, the planting area and distribution regions of winter wheat in Shandong Province （the main producing area of winter wheat in China） in 2016 were extracted by decision tree classification method and supervised classification- maximum likelihood classification method, and the accuracy of the classification results was verified based on ground survey data and data published by the statistics bureau. The results showed that the method of taking the GF-1/WFV images as the main source of data, introducing multi-source information into the decision tree and supervised classification models, and then calculating the planting area of winter wheat in the province was feasible. The total accuracy of remote sensing interpretation of winter wheat in Shandong Province in 2016 reached 92.1 %, and Kappa coefficient was 0.806. The planting area of winter wheat extracted based on the remote sensing images in the province was slightly smaller than the area pro-vided by the statistics department, and the extraction accuracy of the area was 93.0%. Research indicates that GF-1/WFV images have great po-tential for development and application in remote sensing monitoring of planting information of crops in a province.