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The popularization of electric vehicles (EVs) is beneficial to the sustainable development of energy and the environment. China's promotion and development strategy for EVs will serve as a model for other countries. EV ownership has a significant difference between first/second-tier (FST) cities and third/fourth-tier (TFT) cities and there is a huge growth potential for the EV market in those TFT cities. This paper aims to explore the factors influencing the adoption intentions for EVs in FST and TFT cities under a subsidy deduction and to make a comparative analysis of their regional heterogeneity. Based on the extended theory of planned behavior (TPB) model, the structural equation model is used to compare the factors affecting the adoption intention for EVs of 858 respondents in China. The results show that attitude, subjective norms, novelty seeking, non-financial incentive, product cognition, and environmental concerns are positively related to intention in FST and TFT cities; however, infrastructure development only has a positive significant impact in the TFT cities. Additionally, the subsidy deduction has a more negative impact on the adoption intentions in FST cities. Our findings provide vital insights for formulating government regulations and marketing strategies depending on the diverse sizes and attributes of Chinese cities.

Direct quantification of terrestrial biosphere responses to global change is crucial for projections of future climate change in Earth system models. Here, we synthesized ecosystem carbon-cycling data from 1,119 experiments performed over the past four decades concerning changes in temperature, precipitation, CO2 and nitrogen across major terrestrial vegetation types of the world. Most experiments manipulated single rather than multiple global change drivers in temperate ecosystems of the USA, Europe and China. The magnitudes of warming and elevated CO2 treatments were consistent with the ranges of future projections, whereas those of precipitation changes and nitrogen inputs often exceeded the projected ranges. Increases in global change drivers consistently accelerated, but decreased precipitation slowed down carbon-cycle processes. Nonlinear (including synergistic and antagonistic) effects among global change drivers were rare. Belowground carbon allocation responded negatively to increased precipitation and nitrogen addition and positively to decreased precipitation and elevated CO2. The sensitivities of carbon variables to multiple global change drivers depended on the background climate and ecosystem condition, suggesting that Earth system models should be evaluated using site-specific conditions for best uses of this large dataset. Together, this synthesis underscores an urgent need to explore the interactions among multiple global change drivers in under-represented regions such as semi-arid ecosystems, forests in the tropics and subtropics, and Arctic tundra when forecasting future terrestrial carbon-climate feedback. ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31430015, 31830012]; US NSFNational Science Foundation (NSF) [DEB-0955771]; ClimMani COST actionEuropean Cooperation in Science and Technology (COST) [ES1308] ; We thank J. Wang (Hebei University), S. Yang (Institute of Botany, Chinese Academy of Sciences), L. Zhou (East China Normal University), C. Qiao (Xinyang Normal University) and H. Li (Henan University) for their help in meta-analyses and interaction analyses, and H. Li, Y. Liu (Institute of Tibetan Plateau Research, Chinese Academy of Sciences) and Y. He (Peking University) for their help in plotting figures. This work was financially supported by the National Natural Science Foundation of China (grant nos. 31430015 and 31830012). This study emerged from the INTERFACE Workshop in Beijing, China (https://www.bio.purdue.edu/INTERFACE/) supported by the US NSF DEB-0955771. We also acknowledge support from the ClimMani COST action (ES1308). ; Public domain authored by a U.S. government employee