Wu Ai-Bin, Chen Fu-Guo, Zhao Yan-Xia, Qin Yan-Jie, Liu Xin, Guo Xiao-Ping
School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China.
Hebei Engineering Research Center for Geographic Information Application/Institute of Geographical Sciences Hebei Academy of Sciences, Shijiazhuang 050011, China.
Huan Jing Ke Xue. 2024 May 8;45(5):2828-2839. doi: 10.13227/j.hjkx.202305221.
It is of great practical significance for regional sustainable development and ecological construction to quantitatively analyze the impact of construction land expansion on terrestrial ecosystem carbon storage and to explore the optimization scheme of simulating construction land expansion to improve future ecosystem carbon storage. Based on the land use and cover change (LUCC) and other geospatial data of the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2020, this study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the patch-generating land-use simulation (PLUS) model to assess and analyze the changes in ecosystem carbon stocks and spatial patterns regionally. In this study, we performed linear regression analysis to investigate the relationship between urban land expansion and changes in ecosystem carbon stocks for varying urban land proportion levels during two distinct time intervals, 2000-2010 and 2010-2020, which was conducted at a spatial resolution of 2 km. Three distinct urban land expansion scenarios were subjected to simulation to forecast the prospective land use pattern by 2030. Subsequently, we quantified the ramifications of these scenarios on ecosystem carbon stocks during the period from 2020 to 2030. The results were as follows:① In the Beijing-Tianjin-Hebei Urban Agglomeration, the ecosystem carbon stocks exhibited notable variations over the study period, with values of 2 088.02, 2 106.78, and 2 121.25 Tg recorded for the years 2000, 2010, and 2020, respectively, resulting in a cumulative carbon sequestration of 33.23 Tg C during the study duration. It is noteworthy that forest carbon storage emerged as the dominant contributor, with an increase from 1 010.17 Tg in 2000 to 1 136.53 Tg in 2020. Throughout the study period, the spatial distribution of carbon stocks displayed relative stability. Regions characterized by lower carbon content were concentrated in the vicinity of the Bohai Rim region and in proximity to cities such as Beijing, Tianjin, and Shijiazhuang, as well as rural settlements. In contrast, grid units with moderate and high carbon stocks were predominantly situated in the western Taihang Mountain and the northern Yanshan Mountain. Additionally, there was a tendency of increasing carbon stocks in the Taihang Mountain and Yanshan Mountain region, whereas those surrounding major urban centers such as Beijing, Tianjin, Shijiazhuang, and Tangshan experienced a notable decline in carbon stocks. Such reductions were most pronounced in regions undergoing urban land expansion during the study period. ② In grid units with an urban land proportion exceeding 10% at each level, a strong correlation was observed between urban land expansion and changes in carbon stocks during both the 2000-2010 and 2010-2020 periods. The changes in urban land proportion adequately explained the variations in carbon stocks. However, the explanatory power of urban land on carbon stocks decreased during the 2010-2020 period, indicating that other factors played a more substantial role in influencing carbon stocks during this time. The regression coefficients for both periods exhibited a fluctuating upward trend. In comparison to that during the 2000-2010 period, the impact of urban land expansion on carbon stocks was relatively smaller during 2010-2020, indicating a weakening influence. ③ In light of three distinct development scenarios, namely natural development (Scenario Ⅰ), a 15% reduction in the rate of urban land expansion (Scenario Ⅱ), and a 30% reduction in the rate of urban land expansion (Scenario Ⅲ), the projected ecosystem carbon stocks for the Beijing-Tianjin-Hebei Urban Agglomeration in the year 2030 were estimated to be 2 129.12, 2 133.55, and 2 139.10 Tg, respectively. These projections indicated an increase of 7.88, 12.30, and 17.85 Tg in comparison to the current carbon stocks. All scenarios demonstrated that the terrestrial ecosystem would play a role of carbon sink, particularly with the greatest carbon sink observed in the scenario with a 30% reduction in urban land expansion. The fit performance between urban land expansion and carbon stock changes during the 2020-2030 period was significantly better than that during the 2000-2010 and 2010-2020 periods, and the regression coefficients showed a fluctuating increase with an increase in urban land proportion. Across grid units with different urban land proportion levels, the regression coefficients exhibited the order of Scenario Ⅰ < Scenario Ⅱ < Scenario Ⅲ. In pursuit of the carbon peaking and carbon neutrality goals, the Beijing-Tianjin-Hebei Urban Agglomeration should prioritize scenarios with reduced rates of urban land expansion, especially in regions with higher urban land proportions.
定量分析建设用地扩张对陆地生态系统碳储量的影响,并探索模拟建设用地扩张以改善未来生态系统碳储量的优化方案,对于区域可持续发展和生态建设具有重要的现实意义。基于2000—2020年京津冀城市群土地利用与覆盖变化(LUCC)等地理空间数据,本研究利用生态系统服务与权衡综合评估(InVEST)模型和土地利用模拟(PLUS)模型,对区域生态系统碳储量变化及空间格局进行评估与分析。本研究进行线性回归分析,以探究2000—2010年和2010—2020年两个不同时间段内,不同城市土地比例水平下城市土地扩张与生态系统碳储量变化之间的关系,分析在2 km的空间分辨率下进行。模拟了三种不同的城市土地扩张情景,以预测2030年的未来土地利用格局。随后,我们量化了这些情景在2020—2030年期间对生态系统碳储量的影响。结果如下:①在京津冀城市群,研究期间生态系统碳储量呈现显著变化,2000年、2010年和2020年的碳储量分别为2 088.02 Tg、2 106.78 Tg和2 121.25 Tg,研究期间累计碳固存33.23 Tg C。值得注意的是,森林碳储量是主要贡献者,从2000年的1 010.17 Tg增加到2020年的1 136.53 Tg。在整个研究期间,碳储量的空间分布表现出相对稳定性。碳含量较低的区域集中在渤海湾地区附近以及北京、天津、石家庄等城市周边和农村居民点。相比之下,碳储量中等和较高的网格单元主要位于太行山以西和燕山以北。此外,太行山和燕山地区的碳储量有增加趋势,而北京、天津、石家庄和唐山等主要城市中心周边的碳储量则显著下降。在研究期间进行城市土地扩张的地区,这种下降最为明显。②在各层级城市土地比例超过10%的网格单元中,2000—2010年和2010—2020年期间城市土地扩张与碳储量变化之间均存在强相关性。城市土地比例的变化充分解释了碳储量的变化。然而,2010—2020年期间城市土地对碳储量的解释力下降,表明在此期间其他因素在影响碳储量方面发挥了更重要的作用。两个时期的回归系数均呈现波动上升趋势。与2000—2010年期间相比,2010—2020年期间城市土地扩张对碳储量的影响相对较小,表明影响在减弱。③根据三种不同的发展情景,即自然发展情景(情景Ⅰ)、城市土地扩张速率降低15%(情景Ⅱ)和城市土地扩张速率降低30%(情景Ⅲ),预计到2030年京津冀城市群的生态系统碳储量分别为2 129.12 Tg、2 133.55 Tg和2 139.10 Tg。这些预测表明,与当前碳储量相比分别增加了7.88 Tg、12.30 Tg和17.85 Tg。所有情景均表明陆地生态系统将发挥碳汇作用,特别是在城市土地扩张速率降低30%的情景中碳汇最大。2020—2030年期间城市土地扩张与碳储量变化之间的拟合性能明显优于2000—2010年和2010—2020年期间,回归系数随城市土地比例增加呈现波动上升。在不同城市土地比例水平的网格单元中,回归系数呈现情景Ⅰ<情景Ⅱ<情景Ⅲ的顺序。为实现碳达峰和碳中和目标,京津冀城市群应优先选择城市土地扩张速率降低的情景,特别是在城市土地比例较高的地区。