Carbon Footprint of Mediterranean Pasture-Based Native Beef: Effects of Agronomic Practices and Pasture Management under Different Climate Change Scenarios

Knowledge domain and emerging trends of carbon footprint in the field of climate change and energy use: a bibliometric analysis

Carbon footprint (CF) research has attained tremendous popularity for improving the climate environment purposes. In particular, current energy use has been identified as the main cause of climate change. CF plays an irreplaceable role in managing energy use, reducing gas emissions, and improving climate change. The objective of this study was to review studies that have developed CF and to perform a bibliometric analysis using two key terms: "climate change" and "energy use". From bibliometric analysis using CiteSpace and VOSviewer, it was possible to establish a knowledge map of cooperative network structure and research evolution. We are aiming to reveal the main logical chain of CF research leading to climate change, to make up for the lack of current literature, and provide research inspiration for researchers. The research findings mainly focus on four aspects. First, the relevant research began in 2008 and is in a state of continuous rise. Second, due to the law of research development and the prominence of practical problems, related research has experienced a stage from conceptual methods to specific problems. Third, China and the USA assume an important role in which international cooperation is the overall trend. Fourth, related research can be divided into CF algorithm research, ecological environment management research, and specific cross-industry fields. In addition, possible opportunities for change in related research are explored. It is also suggested that the integration of CF with other footprints, when energy use and environmental change are fully considered, may become an important future research trend by providing a more comprehensive environmental impact.

The Tapio Decoupling Principle and Key Strategies for Changing Factors of Chinese Urban Carbon Footprint Based on Cloud Computing

The expansion of Xi’an City has caused the consumption of energy and land resources, leading to serious environmental pollution problems. For this purpose, this study was carried out to measure the carbon carrying capacity, net carbon footprint and net carbon footprint pressure index of Xi’an City, and to characterize the carbon sequestration capacity of Xi’an ecosystem, thereby laying a foundation for developing comprehensive and reasonable low-carbon development measures. This study expects to provide a reference for China to develop a low-carbon economy through Tapio decoupling principle. The decoupling relationship between CO₂ and driving factors was explored through Tapio decoupling model. The time-series data was used to calculate the carbon footprint. The auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing. A general multilayer perceptron neural network realized by a parallel BP learning algorithm was proposed based on Map-Reduce on a cloud computing cluster. A partial least squares (PLS) regression model was constructed to analyze driving factors. The results show that in terms of city size, the variable importance in projection (VIP) output of the urbanization rate has a strong inhibitory effect on carbon footprint growth, and the VIP value of permanent population ranks the last; in terms of economic development, the impact of fixed asset investment and added value of the secondary industry on carbon footprint ranks third and fourth. As a result, the marginal effect of carbon footprint is greater than that of economic growth after economic growth reaches a certain stage, revealing that the driving forces and mechanisms can promote the growth of urban space.