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将基于自然的解决方案纳入碳的社会成本。

Embedding nature-based solutions into the social cost of carbon.

机构信息

Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong.

Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong.

出版信息

Environ Int. 2022 Sep;167:107431. doi: 10.1016/j.envint.2022.107431. Epub 2022 Jul 28.

Abstract

China, the world's largest CO emitter, is making every effort to transition to a low-carbon economy and fulfill its part of a concerted global commitment to combating climate change. In tandem with decarbonizing energy and industries, feasible supplementary measures are urgently needed to help remove anthropogenic CO from the atmosphere. A burgeoning literature has emphasized the CO removal capability of land re-naturalization (such as afforestation and wetland restoration), thereby regarding cognate land-use conversions as Nature-based Solutions (NbS) and potential climate policy options. However, little empirical evidence exists concerning the effectiveness of different land re-naturalization pathways (such as converting wetlands to forests or agricultural lands to grasslands), and it also remains unclear how NbS alternatives (i.e., land-use conversions resulting in negative CO emission) and non-NbS options (i.e., land-use conversions resulting in positive CO emission) could affect the social cost of carbon (SCC), a conventional measurement for prescribing carbon mitigation approaches. This study aims to fill in this knowledge gap via embedding NbS into the dynamic integrated climate-economics (DICE) model to quantify their impacts on the SCC. Using the Pearl River Delta region (south China) as a case study for the temporal horizon during 2000-2020, we find that both positive and negative CO fluxes have been brought by different natural/semi-natural land conversions, affecting the SCC correspondingly. A total of 7 out of 17 types of land-use conversions could be identified as feasible NbS interventions, including forest restoration, forest-to-wetland, grassland-to-forest, grassland-to-wetland, grassland-to-cropland, cropland-to-forest, and cropland-to-wetland conversions, which could reduce the SCC values (comparing 2020 base-year with 2000 base-year) by 0.0132, 0.0009, 0.0033, 0.0030, 0.0001, 0.0082, and 0.0001 (USD/tCO), respectively. While the SCC is mainly determined by energy and industrial structure, the overall effect of NbS is larger than the sum of land urbanization and non-NbS land-use conversions. Via embedding the real-world inter-dynamics of land-use conversions into the SCC quantification, this study presents a pioneer assessment of the impacts of NbS on the SCC in an integrated framework, sheds important insights into the effectiveness of NbS, and offers practical implications for policy-makers to devise comprehensive policies covering all feasible CO abatement options.

摘要

中国作为世界最大的二氧化碳排放国,正在努力向低碳经济转型,履行其在全球应对气候变化方面的共同承诺。除了减少能源和工业的碳排放外,还需要采取可行的补充措施,以帮助从大气中清除人为二氧化碳。越来越多的文献强调了土地再自然化(如造林和湿地恢复)的二氧化碳去除能力,因此将相关的土地利用转变视为基于自然的解决方案(NbS)和潜在的气候政策选择。然而,关于不同土地再自然化途径(如将湿地转变为森林或将农业用地转变为草地)的有效性,几乎没有实证证据,也不清楚 NbS 替代品(即导致负二氧化碳排放的土地利用转变)和非 NbS 选择(即导致正二氧化碳排放的土地利用转变)将如何影响社会碳成本(SCC),这是一种用于规定碳减排方法的常规衡量标准。本研究旨在通过将 NbS 嵌入动态综合气候-经济(DICE)模型来填补这一知识空白,以量化它们对 SCC 的影响。本研究以中国珠江三角洲地区(南部)作为 2000-2020 年期间的时间范围进行案例研究,发现不同的自然/半自然土地转化带来了正、负二氧化碳通量,相应地影响了 SCC。总共可以确定 7 种土地利用转化类型可作为可行的 NbS 干预措施,包括森林恢复、森林向湿地、草地向森林、草地向湿地、草地向农田、农田向森林和农田向湿地转化,这将使 SCC 值(与 2000 年基准年相比 2020 年基准年)分别降低 0.0132、0.0009、0.0033、0.0030、0.0001、0.0082 和 0.0001(USD/tCO)。虽然 SCC 主要由能源和产业结构决定,但 NbS 的总体影响大于土地城市化和非 NbS 土地利用转化的总和。本研究通过将土地利用转化的实际动态纳入 SCC 量化,在综合框架内对 NbS 对 SCC 的影响进行了开创性评估,深入了解了 NbS 的有效性,并为决策者制定涵盖所有可行的二氧化碳减排方案的综合政策提供了实际意义。

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