Abstract. Based on ARDL model, this paper discussed the relationship of energy consumption, carbon emission and economic growth.The results indicated that the key to reduce carbon emissions lies in reducing energy consumption, optimizing energy structure.

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Hydropower development and effects on fish assemblages by dams in Lancang River Basin have arisen great concerns. As a compensation measure, Xishuangbanna State Nature Reserve was established at Luosuo River in 2007, to protect fish species affected by dams in lower Lancang River. Based on published data of fish since 1950s, we analyzed the fish biodiversity of Luosuo River and the similarity with species of lower Lancang River, using G-F index and Sprensen similarity index respectively. The results clearly show that fish biodiversity is quite high at the genus level in Luosuo River. 60 species (including 20 endemic species) were recorded in both reaches, accounting for 71.43% of total species in lower Lancang River. Similar composition of fish community and high similarity indicate that Luosuo River has the potential to protect most of fish species (including endemic species) under hydropower development in the downstream of Lancang River.

Background: Monitoring armyworm (Mythimna separata Walker) damage in crops requires timely, rapid and accurate observations to avoid severe yield losses. Results: The Random Forest (RF) classifier was more effective at automatically and accurately monitoring armyworm damage compared with Support Vector Machine (SVM), Multilayer Perceptron Classifier (MLPC) and Naive Bayes Classifier (NB) classifiers. Furthermore, the incorporation of an Unmanned Aerial Vehicle (UAV) image-generated digital surface model improved the performance of the RF classifier, increasing the F-score from 0.985 and 0.970 to 0.997 and 0.994, and increasing the Kappa coefficient from 0.955 to 0.990. In addition, we found that Band 3 (735 nm) of the UAV image and Band 6 (740 nm) of a coincident Sentinel-2 image were not sensitive to an armyworm infestation in this study. Conclusions: We developed an accurate algorithm for the automated identification of armyworm-damaged corn plants using UAV images at the field scale. The study also indicated the feasibility of the developed method for monitoring corn armyworm damage at regional scale when combined with Sentinel-2 images.