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As an emerging power generation technology, small module reactors (SMRs) have the potential for development with its contribution to reducing greenhouse gas (GHG) emissions. In this study, an SMR-induced environmental input-output model (SEIOM) is proposed to simulate the environmental consequences of SMRs development and provide suggested schemes for SMRs deployment. A case study of Saskatchewan, Canada is conducted to demonstrate the proposed model. Specifically, key industries with high reduction potentials are first identified in the study; then, the power supply for three energy-intensive industries is assumed to be replaced by power generated from SMRs at various penetration degrees. The corresponding changes in direct and indirect GHG emissions and the interrelationships among multiple economic sectors associated with GHG flows are analyzed. The results indicate that there are close interdependences between various sectors and a small group of sectors could play a big role in GHG emission mitigation. In Saskatchewan, “Electricity power generation, transmission and distribution”, “Oil and gas extraction”, “Potash mining” and “Petroleum refineries” are key sectors for realizing GHG emission reduction targets. Meanwhile, it is estimated that replacing the power supply for “Oil and gas extraction” sector with SMRs would contribute the most to the reduction in GHG emission, which is much more than those for “Potash mining” and “Petroleum refineries” sectors. This study is expected to provide a basis for supporting the initiative and application of SMRs.

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.