To date, unmanned aerial vehicles (UAVs), commonly known as drones, have been widely used in precision agriculture (PA) for crop monitoring and crop spraying, allowing farmers to increase the efficiency of the farming process, meanwhile reducing environmental impact. However, to spray pesticides effectively and safely to the trees in small fields or rugged environments, such as mountain areas, is still an open question. To bridge this gap, in this study, an onboard computer vision (CV) component for UAVs is developed. The system is low-cost, flexible, and energy-effective. It consists of two parts, the hardware part is an Intel Neural Compute Stick 2 (NCS2), and the software part is an object detection algorithm named the Ag-YOLO. The NCS2 is 18 grams in weight, 1.5 watts in energy consumption, and costs about $66. The proposed model Ag-YOLO is inspired by You Only Look Once (YOLO), trained and tested with aerial images of areca plantations, and shows high accuracy (F1 score = 0.9205) and high speed [36.5 frames per second (fps)] on the target hardware. Compared to YOLOv3-Tiny, Ag-YOLO is 2× faster while using 12× fewer parameters. Based on this study, crop monitoring and crop spraying can be synchronized into one process, so that smart and precise spraying can be performed.

Coal-fired power is one of the largest contributors to China's carbon emissions. To promote its national low-carbon transition ambitions, the Chinese government has issued a series of policies to reduce emissions from coal-fired power plants (CFPP) during its 14th Five-Year Plan (2021-2025). This study mainly focuses on the mitigation potential of related national policies, using global optimization methods with double constraints on different policy implementation extents and power supply security under different scheduled views of national new energy developments. Thereby, 81 scenarios are set, and policy simulations till 2025 are conducted, achieving emission reductions ranging from 0.39 Gt to 1.04 Gt across scenarios. Specifically, if all policies are implemented as planned, they can bring significant changes, 0.64 Gt CO2 cumulative reduction and 25 Mt/GWh emitting efficiency improvement. But the simulated emission-changing trend shows that they may not be sufficient for the nation's target of peaking emissions before 2030, while results in higher-extent scenarios indicate that stronger implementation is required for this target. More relevant recommendations are also provided for subsequent sustainability policies on CFPPs in China.

With the recovery of the world economy, the increase in energy demand, global environmental pollution, climate change, and many other contradictory issues, after China proposed the Double-Carbon goal, research on the Energy-Environment-Economy system has attracted much attention. Through the visual analysis of Energy-Environment-Economy research literature, this paper provides scholars committed to this field with research characteristics and hotspots evolution trends in this field, and provides a direction guidance and practical method reference for subsequent research on Energy-Environment-Economy system-related issues. This study selected the literature from the Energy-Environment-Economy system field of Web of Science and CNKI database for nearly 20 years, uses CiteSpace to conduct research bibliometrics and visual analysis, and explores the main characteristics, research hotspots, and evolution trends of Energy-Environment-Economy system in Chinese and English. The research found that: ① there are many research results on Energy-Environment-Economy systems, and the research interest in the field of Energy-Environment-Economy systems continues to rise; ② the number of documents and research institutions in China ranks first in the world, and the influence of research results needs to be improved; ③ the hot topics of research topics in each period are bright, research hotspots evolve from single subsystems to systemic issues; ④ the attention to environment and climate change has increased significantly, and the evolutionary characteristics of research hotspots are in line with the development trend in reality.

Optimizing the industry development system and implementing a high-quality development strategy in China’s coal industry require us to grasp the overall status and regional differences of industrial development. Measuring and decomposing the total factor productivity growth of the coal industry is a necessary prerequisite. In this study, we estimated the total factor productivity (TFP) growth of coal industry in 24 major coal-producing provinces in China by constructing a stochastic frontier analysis (SFA) model based on a translog production function and decomposed it into technological progress change (TC), technical efficiency change (TE), scale efficiency change (SE), and factor allocation efficiency (AE). After analyzing the temporal evolution characteristics of TFP growth and its decomposition terms, we also characterized the spatial characteristics by region and province. The results showed that TFP growth in China’s coal industry is on the rise, with TC growth being the main driving factor of this; additionally, the weak growth of SE and AE also plays a limited role in this increase, while the decrease in TE hinders this trend. There are also significant regional differences in the TFP growth of China’s coal industry, with a ranking of central > west > east > northeast. Drivers of TFP growth vary between regions or in different provinces within the same region.

This study examines the complex relationship between renewable energy penetration (REP) and CO₂ emissions in China, aiming to provide insights into deep decarbonization strategies. Using the advanced and robust pooled mean group-autoregressive distributed lag (PMG-ARDL) estimation, this paper analyzes the long-run equilibrium and short-run dynamics of REP and CO₂ emissions based on panel data from 30 Chinese provinces (1997–2021). Wavelet coherence analysis (WCA) and exploratory spatio-temporal data analysis (ESTDA) are also employed to explore time-frequency relationships and spatial correlation dynamics. The findings indicate that: (1) A 1% increase in REP reduces long-term and short-term carbon emissions by 0.05% and 0.26%, respectively. (2) A more pronounced coherence in the 1–2 year time-frequency band. (3) REP and CO₂ exhibit opposing spatial distribution patterns (−0.224), with distinct north-south clustering, dominated by low-high and low and high clustering, respectively. (4) Most provinces’ LISA time paths demonstrate strong spatiotemporal stability, while Central, South, and Southwest China exhibit a more complex spatial variability process, and the spatiotemporal transitions, predominantly of Type IV, reflect strong locking effects. This paper offers policy suggestions for China’s low-carbon energy system and lessons for other economies.