Improving agricultural green total factor productivity in China: do environmental governance and green low-carbon policies matter?

Strengthening environmental governance and green low-carbon policies is a key measure to improve agriculture green total factor productivity and develop sustainable agriculture. Against this background, this study explores the relationship between agriculture green total factor productivity (AGTFP), environmental governance, and green low carbon policies based on panel data of 30 Chinese provinces from 2005 to 2019, and the system generalized method of moments model is adopted. The empirical results are as follows: (1) Environmental governance will not only directly affect AGTFP, but also indirectly affect AGTFP by influencing green technology innovation. (2) Environmental governance can strengthen the promotion of green low-carbon policies to AGTFP. (3) There are regional differences in causality among AGTFP, environmental governance, and green low-carbon policies. These empirical results offer Chinese policymakers scientific and normative recommendations for improving AGTFP and developing sustainable green agriculture.

How does risk perception of the COVID-19 pandemic affect the consumption behavior of green food?

Attention to health is on the rise with the global pandemic of COVID-19, especially in food security. Green food is viewed as a healthy, safe, and nutritious food, which plays a significant role in enhancing immunity. This study aimed to investigate how risk perception affects the consumption behavior of green food. Risk perception and health awareness were added to the original model based on the extended theory of planned behavior. And an online survey about the influence of COVID-19 on consumers' green food consumption behavior was conducted with 612 valid respondents recruited. The results indicate that risk perception has a positive effect on both consumption intention and behavior. The mediating effect analysis shows that risk perception influences green food consumption intention by improving people's attitudes, subjective norms, and health awareness. These findings can not only help clarify the relationship between green food consumption behavior and the risk perception of COVID-19 but also provide some valuable implications for policymakers and marketers in promoting green food.

Effects of Risk Perception of Pests and Diseases on Tea Famers' Green Control Techniques Adoption.

Green control techniques support the concept of green plant protection, advocate for the safe and reasonable use of pesticides, and finally achieve the goal of controlling pests and diseases and protecting the environment. The purpose of this study is to explore the effect of risk perception of pests and diseases on farmers' usage intention of green control techniques. Based on 747 samples of tea farmers in Sichuan province, China, introducing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework and using the Partial Least Square-Structural Equation Modeling (PLS-SEM) approach, this paper found that risk perception has a negative impact on behavioral intention. Performance expectancy, effort expectancy, and social influence can positively affect behavioral intention, and facilitating conditions can also positively influence usage behavior. Moreover, the mediating analysis indicated that the higher the risk perception is, the less performance expectancy of green control techniques and the weaker the behavioral intention. Meanwhile, risk perception also plays a mediating effect on the relationship between effort expectancy and behavioral intention. This study could help to provide references for policymaking to improve the adoption of green control techniques.

Assessing the relationship between air pollution, agricultural insurance, and agricultural green total factor productivity: evidence from China.

As a favorite means to promote the development of green agriculture, agricultural insurance can not only encourage farmers to adopt green production technology and improve production efficiency, but also achieve the purpose of reducing the input of chemicals to protect the environment. This article aims to study the dynamic relationship between agricultural insurance, air pollution, and agricultural green total factor productivity using the panel vector auto-regressive method (PVAR) and panel data of 30 provinces in China from 2005 to 2018. The empirical results show that there is a significant cross-sectional dependence and the co-integration relationship between agricultural insurance, air pollution, and agricultural green total factor productivity. The increase in agricultural insurance can improve agricultural green total factor productivity and aggravate air pollution to a certain extent. However, serious air pollution does not improve agricultural green total factor productivity. Panel Granger causality test results show that agricultural insurance has a one-way causal relationship with green total factor productivity and air pollution, and so does air pollution with agricultural green total factor productivity. In addition, impulse response results show that increasing agricultural insurance or reducing air pollution can improve agricultural green total factor productivity. These conclusions have long-term practical implications for both agricultural policymakers and environmental managers.

Forecasting Carbon Price in China: A Multimodel Comparison.

With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in understanding the dynamics of the carbon trading market and achieving national emission reduction targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.

The role of crop insurance in reducing pesticide use: Evidence from rice farmers in China.

Pesticides play a significant role in increasing agricultural yields. However, the overuse of chemical pesticides on crops is a major challenge faced by China, which damages human health, threatens food security, and increases production costs. This paper provides empirical evidence on the relationship between crop insurance and pesticide use. Based on micro-survey data collected from 3410 households, we estimate a control function (CF) approach and find that farmers who purchase crop insurance use 33.30% lower pesticides than those who do not. A series of robustness tests confirm these findings. The results of the moderating effect analysis suggest that rice cultivation training, experience, schooling years, and agricultural laborers can negatively moderate the effect of crop insurance on pesticide use. Furthermore, we also find that the treatment effect of crop insurance is stronger for farmers who are most inclined to purchase crop insurance.

Effect of agricultural subsidies on the use of chemical fertilizer.

Agricultural subsidies lead to changes in the use of chemical fertilizer by farmers. Using data from a household survey conducted annually by the Rural Economy Research Center of the Ministry of Agriculture of China from 2014 to 2018, Control Function (CF) approach and Heteroskedasticity-based identification strategy were employed to analyze the impact of agricultural subsidies on chemical fertilizer use by rice farmers. After addressing the problem of endogeneity, we found that agricultural subsidies have a significantly negative impact on the use of chemical fertilizer. Precisely, every 100% increase in agricultural subsidies would result in an average decrease of 3.4% in chemical fertilizer use. In addition, results of heterogeneity analysis showed that agricultural subsidies had a stronger negative impact on fertilizer use as rice-planting experience increases. But the ability of rice-planting management and off-farm labor within household could reduce this negative impact. We also found that the effect of arable land productivity conservation subsidies policy was short-lived. Furthermore, based on the frame of structural equation model (SEM), the results of mediation analysis showed that agricultural machine and rice-planting area had partial mediation of 5.3% and 41%, respectively. It implied that agricultural subsidies reduced fertilizer use by promoting the adoption of agricultural techniques and expanding the planting area. In brief, agricultural subsidies had both technical effect and scale effect. But the mediating effect of household income was not significant. This study is intended to assist the concerned authority and agriculture sector to understand the positive role of agricultural subsidies in sustainable production, and provides some feasible policy proposals.

Effects of haze pollution on pesticide use by rice farmers: fresh evidence from rural areas of China.

Recently, severe haze pollution has not only threatened human health and food security, but also seems to have aggravated the unscientific use of pesticides by rice farmers in rural areas of China. Using original data on haze pollution across China, combined with rural household survey data collected from 2014 to 2018, we conducted a detailed empirical study on the effects of haze pollution on pesticide use by rice farmers based on the theory of risk aversion. The empirical results revealed that haze pollution with higher levels of PM2.5 positively impacted the use of chemical pesticides in rice cultivation. More precisely, with each 100% increase in PM2.5 concentrations, the use of pesticide per mu increased by 7.9%, and the average pesticide cost per mu increased by 2.3%. The results were robust to a series of tests that addressed potential endogeneity concerns, including omitted variable bias, measurement error, and reverse causality. We then examined the heterogeneous effects of haze pollution increases on the use of chemical pesticides and found that for rice farmers without rice insurance, haze pollution has a stronger effect on pesticide use and a weaker effect on pesticide cost. However, for rice farmers with more experience in rice cultivation and small-scale rice planting, the effect of haze pollution on pesticide use and cost is relatively small. Our findings provide important policy implications for pesticide risk management in rural areas of developing countries.