Renewable Energy and Carbon Emissions: New Empirical Evidence from the Union for the Mediterranean

Evaluation and prediction of carbon emission from logistics at city scale for low-carbon development strategy

Low-carbon is a part of China's efforts to pursue the national strategy of "carbon peaking and carbon neutrality." Meanwhile, the path of low-carbon transformation of logistics has become a topic of global concern. This study constructs a technical framework of logistics carbon emissions (LCE), which is composed of carbon emission evaluation, carbon emission prediction and low-carbon strategy. All 13 prefecture-level cities in Jiangsu, China, are the application objects in empirical research. Then, the influence analysis of the LCE efficiency based on the panel Tobit model and the evolution of LCE under different scenarios are explored. The results show that: (ⅰ) during the study period (2013-2020), the LCE in Jiangsu showed an overall upward trend, with Xuzhou, Suzhou and Nanjing being the cities with the highest carbon emissions; (ⅱ) the static efficiency of LCE in Jiangsu is at a medium level, with fluctuations in Suzhou, Changzhou, Zhenjiang, Nantong, and Suqian caused by the technical change index; (ⅲ) economic level, industrial structure, fixed asset utilization rate, and ecological environment in Jiangsu are significantly positively correlated with LCE efficiency, while education popularization and energy intensity are negative; (ⅳ) LCE in Jiangsu has been drastically reduced in the low-carbon scenario compared to the baseline scenario. On the above basis, this study proposes suggestions for the low-carbon development strategies of logistics in Jiangsu.

The mechanism of China's renewable energy utilization impact on carbon emission intensity: Evidence from the perspective of intermediary transmission

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies.

Ecological footprint-technological innovations nexus: new empirical evidence from panel data estimations

The aim of this approach is to establish a relation between ecological footprint and technological innovations by using panel data techniques and taking Union for the Mediterranean member countries as the case. On the one hand, the Mediterranean region is an alarming region when it comes to environmental degradation; on the other hand, existing intergovernmental organizations such as the Union for the Mediterranean may provide a sound ground to take common action among all members against such a degradation. The approach takes eleven member countries from the Union for the Mediterranean (Bosnia and Herzegovina, France, Greece, Israel, Italy, Jordan, Morocco, Portugal, Spain, Tunisia, Türkiye) and observes them for the time frame 1992-2020. By using panel unit root tests and cross-sectional dependency tests, the data is being analyzed for further evaluation using mean group, common correlated effect mean group, and augmented mean group analysis. Regarding the observed variables, we could not identify a common trend for these eleven countries. For the sample of eleven countries, the impact of technological innovations was not significant, but country-level augmented mean group analysis results indicated that for Portugal, this impact was significant and positive.