Studying the influencing factors of carbon dioxide emissions is not only practically but also theoretically crucial for establishing regional carbon-reduction policies, developing low-carbon economy and solving the climate problems. Therefore, we used a geographical detector model which is consists of four parts, i.e., risk detector, factor detector, ecological detector and interaction detector to analyze the effect of these social economic factors, i.e., GDP, industrial structure, urbanization rate, economic growth rate, population and road density on the increase of energy consumption carbon dioxide emissions in industrial sector in Inner Mongolia northeast of China. Thus, combining with the result of four detectors, we found that GDP and population more influence than economic growth rate, industrial structure, urbanization rate and road density. The interactive effect of any two influencing factors enhances the increase of the carbon dioxide emissions. The findings of this research have significant policy implications for regions like Inner Mongolia.

Severe natural disasters and related secondary disasters are a huge menace to society. Currently, it is difficult to identify risk formation mechanisms and quantitatively evaluate the risks associated with disaster chains; thus, there is a need to further develop relevant risk assessment methods. In this research, we propose an earthquake disaster chain risk evaluation method that couples Bayesian network and Newmark models that are based on natural hazard risk formation theory with the aim of identifying the influence of earthquake disaster chains. This new method effectively considers two risk elements: hazard and vulnerability, and hazard analysis, which includes chain probability analysis and hazard intensity analysis. The chain probability of adjacent disasters was obtained from the Bayesian network model, and the permanent displacement that was applied to represent the potential hazard intensity was calculated by the Newmark model. To validate the method, the Changbai Mountain volcano earthquake–collapse–landslide disaster chain was selected as a case study. The risk assessment results showed that the high-and medium-risk zones were predominantly located within a 10 km radius of Tianchi, and that other regions within the study area were mainly associated with very low-to low-risk values. The verified results of the reported method showed that the area of the receiver operating characteristic (ROC) curve was 0.817, which indicates that the method is very effective for earthquake disaster chain risk recognition and assessment.

Reference evapotranspiration (ET0) plays an important role in regional dry/wet conditions. Based on the Food and Agriculture Organization of the United (FAO) Penman-Monteith method and daily climate variables, ET0 was calculated for 21 stations in and around the Songnen Grassland, northeast China, during 1960–2014. The temporal and spatial variations of ET0 and precipitation (P) were analyzed in the annual, seasonal, and growing season (from April to October) time series using the Mann-Kendall test, Sen’s slope estimator, and linear regression coupled with a break trend analysis. A sensitivity analysis was used to detect the key climate parameter contributing to ET0 change. By linear regression analysis on the relationship between ET0, P, and the aridity index (AI), the role of ET0 in determining regional wet/dry conditions was analyzed. Results show a higher ET0 in the southwest and a lower ET0 in the northeast, but P was opposite to that of ET0. Evident decreasing trends of ET0 in the annual, seasonal, and growing season time series were detected in almost the entire region by the trend analysis methods. For the entire region, the decreasing trend of ET0 can be linked to the relative humidity and maximum air temperature. The positive contribution of increasing temperature to ET0 was offset by the effect of the significantly decreasing relative humidity, wind speed, and sunshine duration at the 0.05 level during 1960–2014. In addition, the value of ET0 was higher in drought years and lower in wet years.

Reference evapotranspiration (ET0) plays an irreplaceable role in regional dry/wet conditions under the background of climate change. Based on the FAO Penman-Monteith method and daily climate variables, ET0 was calculated for 22 stations in and around Songnen Grassland, northeast China, during 1960-2014. The temporal and spatial variations of ET0 and precipitation (P) were comprehensively analyzed at different time scales by using the Mann-Kendall test, Sen’s slope estimator, and linear regression coupling with break trend analysis. Sensitivity analysis was used to detect the key climate parameter attributed to ET0 change. Then, the role of ET0 in regional dry/wet conditions was discussed by analyzing the relationship between ET0, P and aridity index (AI). Results shown a higher ET0 in the southwest and a lower in the northeast, but P was opposite to that of ET0. Evidently decreasing trend of ET0 at different time scales was detected in almost the entire region, and the significant trend mainly distributed in the eastern, northeastern and central. For the whole region, sensitivity analysis indicated decreasing trend of ET0 was primarily attributed to relative humidity and maximum air temperature. The positive contribution of increasing temperature rising to ET0 was offset by the effect of significantly decreasing relative humidity, wind speed and sunshine duration. In addition, the value of ET0 shown higher in drought years and lower in wet years.