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The balance between agricultural development and maintenance of agro-ecological environment becomes a huge challenge because of global climate changes. Existing literature on the low-carbon pilot policy proposed by the government of China and whether the problems of agricultural development and agricultural environmental protection can be solved or not have not been reviewed. This paper analyzes the impact of low-carbon pilot policies on agricultural eco-efficiency by using SARAR model based on the data of 281 cities in China. Results show the spatial spillover effects between low-carbon pilot policies and agricultural eco-efficiency. The implementation of low-carbon pilot policy can improve agricultural eco-efficiency. Although restrained by agricultural economic development, this policy has disequilibrium effect on agricultural eco-efficiency, has a relatively large impact on agricultural eco-efficiency in western China and other poor areas, and promotes the reduction of agricultural carbon emission. The effect of the implementation of low-carbon pilot policy is affected by the initial agricultural economic development and urban economic conditions. This research aims to improve the agricultural eco-efficiency and enforce the green development of the agricultural economy via the perspective of the low-carbon pilot policy. [ABSTRACT FROM AUTHOR]

The implementation of the "Belt and Road" strategy not only promotes the coordinated development of China's regional economy but also generates a large amount of greenhouse gas emissions. The research and policy recommendations on agricultural carbon emissions are of great significance for China toward engendering sustainable agricultural development and appreciably contributing to the alleviation of global climate change. In this paper, agricultural carbon emissions in the Belt and Road region of China from 2003-2018 are calculated, and its spatial-temporal characteristics are analyzed. Then, we modified the logarithmic mean Divisia index model from the original four factors to six to analyze the drivers of agricultural carbon emissions. The research results obtained are as follows: (1) Total carbon emission reached a peak of 26.6326 million tons in 2016, representing a 54.53% increase from the 17.2342 million tons in 2003. Moreover, it indicated a downward trend in 2017 and dropped by approximately 4% to 25.5685 million tons in 2018. (2) Spatially, agricultural carbon emission varies greatly, with the highest value recorded in Heilongjiang and the lowest in Tibet, thereby exhibiting the characteristics of "northwest>northeast>southwest>southeast." (3) Overall, the economic factor was the most important driving factor, whereas the subsidy factor was the strongest inhibiting factor for the growth of agricultural carbon emissions. Notably, the foreign trade factor had both promoting and inhibiting effects on it. [ABSTRACT FROM AUTHOR]

Climate change continuously threatens sustainable development. As the largest energy consumer and carbon emitter in the world, China is facing increasing pressure to cut carbon emissions. Based on Moran's index I and geographically weighted regression, this paper investigates the spatiotemporal characteristics and the dominating factors of China's province-level carbon intensity in the construction industry from 2005 to 2014, which is aimed at providing a scientific basis for government while implementing a regional-oriented carbon emissions reduction strategy. The empirical results are shown as follows. Firstly, carbon intensity in the construction industry of each province has been decreasing in the past 10 years. Secondly, provincial carbon intensity in this sector shows significant positive spatial autocorrelation characteristics and the degree of spatial clustering of carbon intensity tended to weaken in this period. Third, according to the analysis of the geographically weighted regression (GWR) model, carbon intensity is positively affected by energy intensity while the labor input and production efficiency both have negative effect. Particularly the regression coefficient of labor input is almost twice as large as the other two factors. The results reveal that there is a significant spatial disparity of these three factors in different provinces. [ABSTRACT FROM AUTHOR]