The use of harvested wood products (HWPs) influences the carbon flux. China is both the major producer and trader of HWP, so estimating the carbon stock change of China’s HWP is important to help curb climate change. Accurate reporting and accounting of carbon flows in the HWP pool is needed to meet greenhouse gas monitoring and climate change mitigation objectives under the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. This study applied production approach (PA) to estimate the carbon stock change of China’s HWP from 1900 to 2016. During the estimating period, the carbon stock of HWP in use and deposed at solid waste disposal sites (SWDS) were 649.2 Teragrams Carbon (TgC) (346.8 TgC in wood-based panels, 216.7 TgC in sawnwood and 85.7 TgC in paper & paperboard) and 72.6 TgC, respectively. The carbon amount of annual domestic harvest HWP varied between 87.6 and 118.7 TgC. However, the imported carbon inflow increased significantly after the 1990s and reached 47.6 TgC in 2016, accounting for 46% of the domestic harvest of that year. China has great mitigation potential from HWP and use of this resource should be considered in future strategies to address climate change.

Situated at middle-to-high latitudes with limited thermal resources, Northeast China is the primary maize-producing region in China. It is also one of the regions most significantly impacted by climate change. Given the persistent impact of climate change, it is crucial to elucidate the effects of the varying thermal conditions and low temperatures for different sowing dates on the growth, development, and grain maturity of spring maize. To ensure secure maize production and disaster prevention, choosing the optimal sowing time for spring maize holds significant implications for the judicious utilization of climatic resources, risk mitigation, and the provision of meteorological guidance. Moreover, it can serve as a technical reference for relevant departments to conduct climate evaluation, disaster monitoring, prediction, and assessment, as well as impact analysis of corn production safety. Additionally, it can provide meteorological evidence to ensure food security and promote the sustainable development of modern agriculture. An interval sowing experiment of spring maize was conducted in Harbin in the north of Northeast China. Two varieties were used in the experiment. Four sowing dates were set, and the interval between adjacent sowing dates was 10 days. The local perennial sowing time, 5 May, was set as the second sowing date, with one date set later and two dates set earlier. During the experiment, the growth process, grain dry matter, seed moisture content, yield components, and temperature of spring maize were observed. The impact of temperature conditions on maize growth and yield formation was analyzed in this paper through mathematical statistics, which further led to the establishment of a monitoring and evaluation model for assessing the effect of thermal conditions and temperature on maize. The results showed that the growth rate of spring maize was closely related to temperature. When the average temperature, minimum temperature, and maximum temperature increased by 1 °C, the average emergence rate increased by 1.05%, 0.99%, and 1.07%, respectively, and the average vegetative growth rate increased by 0.16%, 0.16%, and 0.09%, respectively. The change rate of ≥10 °C active accumulated temperature was significantly correlated with the change rate of the dry weight of the grain kernel, which conformed to the quadratic equation of one variable. The temperature influence coefficients of different sowing dates varied from 1.0% to 1.7%. The relationship between the accumulated values of 10 ℃ active accumulated temperature and the grain moisture content of spring maize was a logarithmic function. From 10 to 50 days after anthesis, the effect of temperature can explain about 95% of the change in grain moisture content. After physiological maturity, the effect of thermal conditions can only explain 56–83%. The temperature influence coefficient ranges from 1.3% to 13.8%. Comparatively speaking, the second sowing date is the most suitable sowing date. Early sowing is prone to encounter low temperatures, resulting in underutilization of the early heat, while late sowing is prone to less heat. Both conditions are not conducive to better improve the yield of spring maize.