中国可再生能源在应对气候变化中的角色:第12个五年计划及以后

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“level climate change”关注的是气候变化问题,特别是中国在应对全球变暖中的角色,尤其是在第十二个五年计划(2011-2015年)期间及至2020年的可再生能源发展。文章由来自山东理工大学土木工程学院和热能工程学院的作者撰写。 在全球范围内,气候变化已经成为最紧迫的环境问题。作为世界上最大的二氧化碳排放国和主要能源消耗国,中国面临着转变以煤炭为主导的电力结构的强大压力。为了减少碳排放,风能、太阳能等可再生能源是适宜且必要的选择。文章对不同类型的可再生能源进行了介绍,包括它们的特点,并基于这些介绍,阐述了在中长期目标以及其对减缓气候变化的影响。 在2011年至2020年的关键时期,中国设定了可再生能源发展的具体目标,这些目标旨在推动清洁能源的广泛应用,以降低温室气体(GHG)排放。文章详细讨论了在此期间中国所面临的挑战,尤其是水电、太阳能和风能这三个在规划中被特别强调的领域。对于这些挑战,作者提出了相应的解决建议。 风能作为一种无碳排放的能源,其大规模开发可以显著减少化石燃料的使用。然而,风力发电的不稳定性对电网稳定性和电力调度提出了挑战,需要通过储能技术和智能电网技术来解决。太阳能,尤其是光伏能源,具有广阔的应用前景,但成本高、效率低和技术成熟度不足是当前面临的主要问题。水电虽然清洁且稳定,但在建设和运营过程中可能引发环境和生态问题,如水坝建设对河流生态系统的影响,需要平衡经济效益与环境保护。 为了克服这些挑战,中国采取了一系列政策和措施,如提高可再生能源的研发投入,推动技术创新,优化能源结构,建立和完善市场机制,鼓励绿色能源的投资和消费。此外,国际合作也是解决气候变化问题的关键,通过共享技术和经验,共同推进全球清洁能源的发展。 "level climate change"涉及的主题是气候变化背景下中国可再生能源的战略定位和发展策略。文章深入探讨了中国如何通过发展风能、太阳能等可再生能源来应对全球变暖,以及在实施过程中遇到的困难和解决策略,为中国的低碳转型提供了理论支持和实践指导。

The LULC simulation data we utilized to create future EN maps was produced by X. Liu et al. (2017), which was conducted at the national level. The reason we apply national-level simulated data to a local area is as follows. Firstly, China has a top-down land use planning system (also known as spatial planning) with five levels. The quantitative objectives in national plans are handed down to county-level plans through provincial and prefectural level plans (Zhong et al., 2014). That means land use patterns of nine cities in WUA are required to reflect relevant upper-level plans, for example, to satisfy the land use quota made by Hubei provincial plans and the national plans. Secondly, there are interdependencies across places so what happens in one region produces effects not only on this location but on other regions (Overman et al., 2010). And the increase of construction land in one place will shift protection pressure on natural ecosystems elsewhere for a sustainable goal. The land use simulation at the national level allocated land resources from a top-down perspective and links land use changes in a region to events taking place in other locations through global simulation. However, the Kappa coefficient of the simulated data in WUA is 0.55 and the overall accuracy is 0.71, which is lower than the statistic value at the national-level data. Although the Kappa between 0.4~0.6 is moderate and at an acceptable level (Appiah et al., 2015; Ding et al., 2013; Ku, 2016), the simulated accuracy of the land use data needs to be improved. Future work on exploring the impact of LULC dynamics on EN will develop based on the high-accuracy simulated data and updating the initial simulated time to 2020, by integrating the impacts of socioeconomic factors, climate change, regional planning, land use policy, etc.

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