Lianhong Lv

Climate change can modify regional wind power generation ability, as it may affect wind speed. Here, we developed a multivariate copula downscaling (MvCD) approach to statistically downscale the near-surface wind speed of CMIP5 global climate models (GCMs) to the scale of wind farms in Urumqi, China. The low computational cost and high random analysis capability of this approach allowed the rapid assessment of projected changes and randomness from nine GCMs, spanning a range of potential futures under four scenarios. Simulation data from multiple GCMs and historical data of the study area were incorporated into the MvCD to generate a high dimensional multivariate copula. Thereafter, the high dimensional multivariate copula was further used to identify future wind speed patterns based on multiple GCMs under different CO₂ emission scenarios. The estimated amount of wind power generation was obtained using future wind speed data. Results revealed the regional characteristics and periodicity of wind speed for Urumqi in the future. Wind power generation results revealed the impacts of climate changes on regional wind power generation and indicated that high wind speeds would occur from June to September and low wind speeds would occur from December to March in future scenarios. Wind speed would be more extreme under each scenario in the future than before. The highest and lowest wind speeds will increase and decrease, respectively. Sustained high winds would increase the potential of wind power generation in the future. Wind instability based on CO₂ emission increases will lead to wind power being curtailed and low wind-power generation.

Government environmental information disclosure (GEID) plays an important role in promoting the digital transformation of environmental governance, leading the concept of sustainable development, enhancing public oversight capacity, and promoting democratic decision-making governance. Using provincial panel data from China spanning from 2009 to 2021, we conducted spatial data exploratory analysis and used the dynamic spatial panel model to investigate the spatial–temporal development trends and influencing factors of GEID. The results show that (1) GEID in China exhibits significant spatial agglomeration characteristics, with an “H-H” (High-High aggregation type) agglomeration characteristic observed in three national strategic development regions: Yangtze River Delta, southeast coastal areas, and Beijing–Tianjin–Hebei region. (2) The spillover effect from the southeast coastal provinces gradually radiates to the northwest, resulting in an overall westward movement of GEID. (3) GEID exhibits a significant path-dependency feature in the temporal dimension and a “peer effect” in the spatial dimension. (4) Population size has the greatest impact on GEID. Population size, public participation, and the industrial and transportation sectors positively influence GEID improvement at the local level. However, they generate negative spillover effects to neighbouring provinces. Environmental status and the size of the Real Estate sector have no significant effect. Therefore, China should strengthen regional cooperation, narrow regional disparities, cultivate new quality productive forces, establish a government-led proactive disclosure mechanism under public supervision, and improve the level of GEID at the national level.

In the fight against climate change, future policy directions in the transition toward a green travel- and tourism-based economy include improving tourism-derived CO₂ emission levels and guiding individual low-carbon behavior. In China, people tend to engage in outdoor adventure travel and cultural tourism in natural areas. However, limited information is available on the empirical evaluation of energy use and the CO₂ emissions associated with tourism in protected areas. The present study used a life cycle assessment to explore energy use and CO₂ emissions due to tourism and identify the factors driving low-carbon behavior. To these ends, survey data for the protected areas of the Qinling Mountains from 2014 to 2019 were used. The results showed that energy use and CO₂ emissions in various tourism sectors steadily increased from 2014 to 2019, primarily because of an increase in transportation activity. This study used data derived from the calculation of CO₂ emissions per tourist per trip to identify the various factors jointly contributing to the low-carbon behavior of tourists. These included a low-carbon attitude, low-carbon knowledge, environmental education, and policy reward. The broader implications of this study are that several emission reduction policy options are available to address the challenges inherent in sustainable tourism development and that these policies may be selected according to specific conditions. The low-carbon transformation of recreational facilities at travel destinations, policy rewards, and environmental education can regulate tourist behavior, holding the key to sustainable tourism development in protected areas.

The interplay of multi-reservoirs is critical in reservoir joint disposal and water conservancy projects. As the flood risk of upstream hydrological stations could be transferred and unevenly distributed to downstream tributary stations, flood risk transfer through multi-reservoirs warrants further investigation. This study proposed a copula simulation approach to develop a joint flood risk distribution of multi-reservoirs (spanning Xianyang, Huaxian County, and Zhangjiashan) in a drainage tributary of the Weihe River. Pair-copulas of each reservoir pair were constructed to analyse the correlations between the reservoir sites. The approach was then used to create a joint flood risk distribution for the reservoirs. The flood risk and corresponding flood volume of Zhangjiashan were calculated based on the flood risk levels of Xianyang and Huaxian County. The results indicate that the flood risks of Huaxian County would be transferred to Xianyang and Zhangjiashan to some extent, and Xianyang could mitigate more flood risks from Huaxian County than from Zhangjiashan. The findings have significance for informed decision-making regarding the Zhangjiashan reservoir construction project.

CO₂ contributes a lot to the greenhouse effect. The total CO₂ emissions of the two countries, China and the USA, as the world’s top two economies, have exceeded 40% of the total global carbon emissions. In this context, the exploration of the evolution of carbon emissions from energy consumption in China and the USA and the comparison of the characteristics of carbon emission drivers in different periods play a significant role in the policy formulation and climate change cooperation between China and the USA. In this study, the BP structural breakpoint test was used to divide the carbon emission stages of China and the USA from 1970 to 2019. The generalized Divisia index model (GDIM) was developed to decompose the growth of carbon emissions in China and the USA into eight items, GDP, carbon intensity of GDP, energy use, carbon intensity of energy, population, carbon emissions per capita, GDP per capita, and energy intensity, and to analyze the characteristics and cumulative contribution of carbon emission drivers at each stage. Based on the stage and cumulative characteristics of carbon emissions between China and the USA, the USA should take the initiative to assume the legal responsibility of carbon emissions and further deepen the cooperation with other countries in the field of climate change. China should transform the economic growth mode, optimize the energy structure, and improve the efficiency of resource utilization to help achieve the peaking carbon emissions and the carbon neutrality smoothly.