Detection of meteorological influence on bread wheat quality in Hebei province, China based on the gradient boosting decision tree.

Wheat grain quality is equivalent to grain yield in terms of ensuring food security under climate change but has received less attention. Identifying critical meteorological conditions in key phenological periods to account for the variability in grain protein content (GPC) can provide insight into linkages between climate change and wheat quality. The wheat GPC data from different counties of Hebei Province, China during 2006-2018 and corresponding observational meteorological data were used in our study. Through a fitted gradient boosting decision tree model, latitude of the study area, accumulated sunlight hours during the growth season, accumulated temperature and averaged relative humidity from filling to maturity were suggested as the most relevant influencing variables. The relationship between GPC and latitude was distinguished between areas north and south of 38.0° N. GPC decreased with the increasing latitude in areas south of 38.0° N, where at least accumulated temperatures of 515°C from filling to maturity were preferred to maintain high GPC. Besides, averaged relative humidity during the same phenological period exceeding 59% could generate an extra benefit to GPC here. However, GPC increased with increasing latitude in areas north of 38.0° N and was mainly attributed to more than 1500 sunlight hours during the growth season. Our findings that different meteorological factors played a major role in deciding regional wheat quality provided a scientific basis for adopting better regional planning and developing adaptive strategies to minimize climate impacts.

PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model.

At present particulate matter (PM2.5) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM2.5), and then quantifies the health effects of PM2.5 pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure-response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM2.5 pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM2.5 pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335-248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77-4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.

Indirect Economic Impact Incurred by Haze Pollution: An Econometric and Input-Output Joint Model.

Econometrics and input-output models have been presented to construct a joint model (i.e., an EC + IO model) in the paper, which is characterized by incorporating the uncertainty of the real economy with the detailed departmental classification structure, as well as adding recovery period variables in the joint model to make the model dynamic. By designing and implementing a static model, it is estimated that the indirect economic loss for the transportation sector caused by representative haze pollution of Beijing in 2013 was 23.7 million yuan. The industrial-related indirect losses due to the direct economic losses incurred by haze pollution reached 102 million yuan. With the constructed dynamic model, the cumulative economic losses for the industrial sectors have been calculated for the recovery periods of different durations. The results show that: (1) the longer the period that an industrial department returns to normal output after haze pollution has impacted, the greater the cumulative economic loss will be; (2) when the recovery period is one year, the cumulative economic loss value computed by the dynamic EC + IO model is much smaller than the loss value obtained by the static EC + IO model; (3) the recovery curves of industrial sectors show that the recovery rate at the early stage is fast, while it is slow afterwards. Therefore, the governance work after the occurrence of haze pollution should be launched as soon as possible. This study provides a theoretical basis for evaluating the indirect economic losses of haze pollution and demonstrates the value of popularization and application.