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Adaptive farming behavior is a key strategy for farmers to cope with climate change. This paper aims to explore the potential impacts of climate change information sources on farmers’ adaptive farming behavior. This paper clarifies the internal mechanism of three typical information sources, namely government departments, scientific research institutions and neighborhood communication, affecting farmers' adaptive farming behavior. Based on the sample data of 1200 farmers in the main wheat producing areas of Shaanxi Province, the theoretical conjecture is empirically tested. The results showed that the climate change information from government departments and scientific research institutions has a significant positive impact on farmers' adaptive farming behavior, but in the case of considering the three sources of information at the same time, the information source of scientific research institutions has the strongest positive impact on farmers' adaptive farming behavior. After farmers have access to climate change information exchanged by government departments, scientific research institutions and neighbors, their differences in climate change adaptive farming behavior mainly depend on their climate change cognition rather than subject trust factors. Formally organized climate change information represented by government departments and scientific research institutions has a more significant positive impact on farmers' adaptive farming behavior, but this impact depends largely on whether the information obtained by farmers can improve their climate change awareness. [ABSTRACT FROM AUTHOR]

Reducing food waste is an important initiative to ensure food security and mitigate climate change. Previous studies have largely ignored the influence of reference points on food waste behavior and the role of human-to-human relational interactions, failing to capture their role in guiding residents' food waste behavior (RFWBs). To address this gap, this study proposes a model that examines the role of reference dependence, normative internalization, and symbolic expectations in RFWBs, while introducing network embeddedness as a moderating variable. Using data collected from 981 respondents online, ordinary least squares were used to test the hypotheses. The results showed that reference dependence and normative internalization had a positive effect on reducing RFWBs with regression coefficients of 0.178 and 0.293 respectively, while symbolic expectations also had a positive effect on RFWBs with a regression coefficient of 0.227. Additionally, Network embeddedness played a significant moderating role in reference dependence, normative internalization, and symbolic expectations with RFWBs. This paper is theoretically innovative and the relevant findings fill the current research gap in the field of food waste, providing guidance and reference for government departments to formulate policies and promote residents to implement food waste reduction behaviors. [ABSTRACT FROM AUTHOR]

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]