Agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation: A comprehensive quantile analysis.

This paper investigates the asymmetric inter-linkages and causality between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in four main agricultural countries i.e., China, the United States, Japan and Canada, by employing monthly data from 1990 to 2019. Accordingly, Quantile-on-Quantile (QQ) regression and two causality-in-quantile approaches are applied to conduct a comprehensive quantile analysis of the asymmetric relationship for all quantiles of the above distribution. Our results exhibit a positive association between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in China, the United States and Canada. Besides, the strength of the positive association depends largely on the development level of agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation. However, except for the positive impact between fertilizer consumption and agricultural greenhouse gas emissions, I find a negative nexus between agricultural greenhouse gas emissions and technological innovation in Japan. Compared with the other three sample countries, Japan has done the best in agricultural greenhouse gas emission mitigation. The results also demonstrate that agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation follow a bidirectional quantile-causality relation in all sample countries. Overall, I find that fertilizer consumption does increase agricultural greenhouse gas emissions, and that technological innovation has not played a full role in mitigating greenhouse gas emissions in most countries. Finally, our findings have significant implications for formulating reasonable emission reduction measures in the agricultural sector.

Analysis of influencing factors of the CO2 emissions in China: Nonparametric additive regression approach.

As the maximal carbon dioxide (CO2) contributor in world, China is embracing severe stress from emission reduction. It is increasingly important to study the factors affecting China's CO2 emissions. Many researches had extensively researched the driving forces of CO2 emissions of China. However, majority of the researches adopt a conventional linear method based on time-series or cross-section data for researching the CO2 emissions as well as nearly neglect nonlinear relationships. To surmount the limitations of extant investigations, this research adopts a data-driven nonparametric additive regression approach to examine primary influencing factors of China's CO2 emissions. The results manifest that the nonlinear influence of economy on CO2 emissions is same as the Environmental Kuznets Curve hypothesis. The household consumption level embodies the inverted "U-type" pattern. The industrialization also embodies the overturned "U-type" relationship. Aggregate retail sales of consumer goods present a positive "U-type" effect upon CO2 emissions. Similarly, the urbanization signifies a positive "U-type" nexus upon CO2 emissions. Energy intensity indicates a positive "U-type" nexus. The paper ought to exert more attention to the dynamic effects of the driving forces above in order to abate the CO2 emissions of China. This study will also propose corresponding policies and recommendations according to the dynamic effects.

Analysis of influencing factors of the carbon dioxide emissions in China's commercial department based on the STIRPAT model and ridge regression.

Commercial department assumes the vital part in energy conservation and carbon dioxide emission mitigation of China. This paper applies the time-series data covering 2001-2015 and introduces the STIRPAT method to research the factors of commercial department's carbon dioxide emissions in China. The combination of STIRPAT method and ridge regression is first adopted to research carbon dioxide emissions of commercial department in China. Potential influencing factors of carbon dioxide emission, including economic growth, level of urbanization, aggregate population, energy intensity, energy structure and foreign direct investment, are selected to establish the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model, where ridge regression is adopted to eliminate multicollinearity. The estimation consequences show that all forces were positively related to carbon dioxide emissions in China's commercial department except for energy structure. Energy structure is the only negative factor and aggregate population is the maximal influencing factor of carbon dioxide emissions. The economic growth, urbanization level, energy intensity and foreign direct investment all positively contribute to carbon dioxide emissions of commercial department. The findings have significant implications for policy-makers to enact emission reduction policies in commercial sector. Therefore, the paper ought to take into full consideration these different impacts of above influencing factors to abate carbon dioxide emissions of commercial sector.

Influencing factors of the carbon dioxide emissions in China's commercial department: A non-parametric additive regression model.

The commercial department plays a significant role in China's energy conservation and carbon dioxide emission mitigation. The study employs a panel data set during 2001-2015 and adopts a non-parametric additive regression model to analyse the drivers of commercial department's carbon dioxide emissions in China. Non-parametric additive regression model based on the provincial panel data is first applied to investigate the carbon dioxide emissions in China's commercial department. The consequences demonstrate that the nonlinear impact of economic growth upon carbon dioxide emissions is in accordance with the Environmental Kuznets Curve (EKC) hypothesis. Energy intensity is currently in the left half of the "overturned U-shaped" relationship. In the long run, the overturned "U-shaped" impact of energy intensity may be due to the discrepant level of technological development in different phases. Nevertheless, the urbanization demonstrates an erected "U-shaped" impact on carbon dioxide emissions owing to the further urban commercial consumption and fixed asset investment in recent times. Aggregate population presents a fast-to-slow growth relationship, which is the result that residents require massive commercial goods with the augment in population and income. Energy structure also reveals an erected "U-shape" relationship on account of slight electricity consumption in former times and the large amount of thermal power consumption in later phases. As a result, we should take into full consideration the disparate fluctuant impacts of these influencing indicators when it comes to carbon dioxide emissions in commercial department.